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Agent-Based Configuration of
 (Metaheuristic) Algorithms
           - Doctoral dissertation -


      M.Sc. Dagmar Monett Díaz



   Artificial Intelligence, Computer Science Dept.
            Humboldt University of Berlin
                     25.02.2005
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Metaheuristics
A metaheuristic is “a master strategy that guides and modifies other heuristics
                   (like local search procedures) to produce solutions beyond
                   those that are normally generated in a quest for local optimality“
                                                                                  [Laguna 2002]
Examples of metaheuristics:
       - Traditional approaches:
           •EC (Evolutionary Computation): GA (Genetic Algorithms), ES (Evolution
           Strategies), GP (Genetic Programming), etc.
           •SA (Simulated Annealing), TS (Tabu Search), ANN (Artificial Neural
           Networks), EDA (Estimation of Distribution Algorithms), ACS (Ant Colony
           Systems), etc.
       - Hybrid metaheuristics  recent approaches!

    New research goals in metaheuristics domain :
       - To combine aspects from different metaheuristics, Artificial Intelligence,
         Operations Research techniques, etc.
       - Experimental design for configuring is important
       - Optimization of parameters (i.e. configuration process) is a
         relevant issue
D. Monett Díaz                     Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Configuration of algorithms
  (or “fine-tuning” of algorithms)

  Shortcomings:
         Not all metaheuristic algorithms are auto-adaptive (in particular the
         hybrid approaches)
         Usually, control parameters are set by hand or in the spirit of
         brute-force mechanisms; time-consuming task
         Very few published research works; not yet an established
         research area
         Distributed, remote or parallel execution of configuration
         algorithms: not existing


         Special topic in most recent conferences and workshops
         (e.g. HM’04 at ECAI’04 and PSGEA’05 at GECCO’05);
         current open question!!

D. Monett Díaz                    Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Example: a Tabu Search Algorithm
    • Fixed parameters:
        - Tabu criterion (what is forbidden for a given number of iterations)
        - Nr. of elite solutions (intensification strategy: restarting the search around them)
        - Stop criterion (e.g. maximum number of iterations)
    • Free parameter (i.e. factor in study): Tabu list length or tabu tenure
        - Levels: 10, 20, 50  3 configurations
    • Number of global runs (without varying any parameter): NTrials
     one TS
                 1 TTenure=10              1 TTenure=20                     1 TTenure=50
      run!!
                         &                             &                             &
                   fixed factors                 fixed factors                 fixed factors
                         .                               .                             .
                         .         +                     .             +               .
                         .                               .                             .
        NTrials TTenure=10         NTrials TTenure=20                  NTrials TTenure=50
                         &                             &                             &
                   fixed factors                 fixed factors                 fixed factors




                  ► Which    is the “most acceptable” configuration?

D. Monett Díaz                         Humboldt University of Berlin            Doctoral dissertation, 25.02.2005
Why agents for configuring?
     Could agents substitute humans in their tasks?
     Could they behave as experts would do?
          E.g.
                 • by alleviating the time required while configuring,
                   i.e., the cost of time-intensive fine-tuning?
                 • by entering data and processing results?
                 • by autonomously conducting the required experiments?
                 • by (semi) automating the configuration process?

     Answer: Agent-based configuration !!

     Multiagent Systems: properties that are of interest
           - Distributed / Remote execution
           - Cooperation among the agents
           - Autonomy & Specialization
           - Collaborative exploration / exploitation of the search space

D. Monett Díaz                     Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions




                   user problems




   Motivation:
    Powerful algorithms are needed to                 (metaheuristic) algorithms
     solve several real problems
    Configuring them can be a very
     difficult combinatorial problem


D. Monett Díaz               Humboldt University of Berlin          Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions




                    user problems




   Goals:
    To support the configuration              (metaheuristic) algorithms
     process of these algorithms
     . Autonomously, distributed, remote, etc.
    To (semi)automate both monitoring and
     fine-tuning of parameters and conducting
     experiments
D. Monett Díaz                 Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions




                     user problems                                    +CARPS




   Contributions:
    +CARPS, agent-based approach
                                                 (metaheuristic) algorithms
     for configuring
    . Distributed, collaborative problem solving
    . Necessary information specialization/processing
    . Flexibility: very important

D. Monett Díaz                  Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
+CARPS : Architecture
   +CARPS : Multi-Agent System for Configuring Algorithms
                  in Real Problem Solving
                        +CARPS
                                                                 I/O Layer
                        Algorithm
                       Configuration
                          Layer                       Algorithm
                                                       Solution
                                                        Layer


                                    Communication Layer

   D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In
      Proceedings of the International Conference on Advances in Intelligent Systems –
      Theory and Applications, AISTA'2004, IEEE Computer Society, University of Canberra
      and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg.
   D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In
      Proceedings of the First International Workshop on Hybrid Metaheuristics, HM'2004, at
      the 16th European Conference on Artificial Intelligence, ECAI'2004, Valencia, Spain.
D. Monett Díaz                       Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
+CARPS : Agent interactions
                 Pf , Px , ag. lists                                  GUI
                                                                                                 MAS-User
                                               UM                                                interaction
                     Pf ,   ag. lists
                                     best ag. lists
                                solutions                                        paths to M
                      ABRRHC                                                     and i/o files
                      info and          SM                                       par. names,
                      ag. lists                                                  ag. lists
                                            exch.
                             solutions      solutions                                data

                                                             configs
                             configs                                                          M
                         solutions
                                              AC                                AS
                                                            solutions                                            TS, GA,
                                                                                                                 ES, etc.
                              STi          initial                             results
                                          configs
                                                                                  Algorithm’s execution:
                                                                                     optimization process
                                    STi                                     (user problem’s parameters)
                 ISM                          SCB               Configuration process:
                                                                optimization process
                                                                (metaheuristic’s parameters)
D. Monett Díaz                                  Humboldt University of Berlin               Doctoral dissertation, 25.02.2005
Agent-Based Configuration Algorithm
 Configuration algorithm:
                 Agent-Based Random-Restart Hill-Climbing
  Particularities:
      • Stochastic Hill-Climbing algorithm (restart from different candidate
           solutions)
      • Well-known metaheuristic; easy to implement
      • Construction of a topology (neighbors per solution)
      • Migration policy (AC agents exchange best-so-far obtained solutions)
      • ABRRHC: itself a hybrid metaheuristic

                            Implementing a different           only few changes in
                             configuration algorithm           specialized agent!!!


D. Monett Díaz                 Humboldt University of Berlin     Doctoral dissertation, 25.02.2005
Worth of a metaheuristic
 Factors used to analyze metaheuristic’s performance:
       • quality of the best solution found until a stop criterion is verified
       • time to get the best solution
       • algorithm's time to reach an “acceptable“ solution
       • number of function evaluations performed until a stop criterion is
         verified, etc.
  Best-so-far configuration
       • a worth equation indicates “how good” the metaheuristic is
       • more than one factor at a time is considered
       • minimization procedure
  Example of worth equation (using a weighted sum approach)
                     worth( p )  w1  f s  w2  f t  w3  f v         Consider quality of the
                                                                             solutions, time to get
                                                fx                          them and evaluations
             & Normalization, e.g.:                                  for all repetitions with the
                                          max( f s , f t , f v )      same configuration (i.e. p)
D. Monett Díaz                       Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Agents’ communications

   Interaction Protocols (IPs)
      • Define typical patterns of message exchange between agents
      • Agents are supposed to behave consistently by following such IPs
      • FIPA standards (e.g. Contract Net IP)
      • New applications might need new IPs
         – E.g. agent-based configuration of (metaheuristic) algorithms
      • In +CARPS: IPs are implemented as FSM Behaviors from JADE
      • Agent communications: ACL messages, FIPA compliant


      D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged
         for Configuring. In Proceedings of the Workshop Concurrency, Specification, and
         Programming CS&P'2004, volume 3: Multiagent Systems and Applications, Caputh,
         Germany (Also in Special Issue of Fundamenta Informaticae –to appear–)


D. Monett Díaz                        Humboldt University of Berlin      Doctoral dissertation, 25.02.2005
Agents’ communications
    Most relevant IPs in +CARPS                                 Helper-Booker-Protocol


                                                                                 AS, AC                              UM
                                                                     Initiator                         Participant
        Interaction Protocols                                                             propose
                                                                                 reject-proposal
                           Helper-Booker IP                                          failure

                                                                                  accept-proposal
                            Engagement IP                                            inform

                                                                                             failure
                                                                          [dead-
                                Request IP                                line1]

                                                                                             confirm


                                                                                   cancel     [deadline2]

      - Which agents will participate?
      - Among UM agent (booker) and
        AC and AS agents (helpers)
      - UM registers with the Directory Facilitator
      - Helpers are initiators of the HBIPs; bookers are responders


D. Monett Díaz                       Humboldt University of Berlin                        Doctoral dissertation, 25.02.2005
+CARPS’ Graphical User Interface
+CARPS’ Graphical User Interface
+CARPS’ Graphical User Interface
+CARPS’ Graphical User Interface
+CARPS’ Graphical User Interface
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Metaheuristics that are considered
  Genetic Algorithm (GA)
        • GA optimal parameters  unknown
        • Parameter estimation in chemical reactions (copolymerizations)
                 D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An
                    Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul.,
                    11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany.
                 D. Monett (2001). On the automation of evolutionary techniques and their application to
                    inverse problems from Chemical Kinetics. In Proceedings of the GECCO'01 Graduate
                    Student Workshop, Genetic and Evolutionary Computation Conference, San
                    Francisco, California, USA.
        • Data provided by colleagues from Institute of Polymer Science and
          Technology, Superior Council of Scientific Research, Spain, and
          Biomaterials Center, Havana University, Cuba
  Evolution Strategy (ES)
        • ES optimal parameters  known
        • Function optimization (Rechenberg 94)
        • Data and program provided by I. Santibáñez-Koref, FG Bionik und
          Evolutionstechnik , TU Berlin

D. Monett Díaz                              Humboldt University of Berlin       Doctoral dissertation, 25.02.2005
Example: Configuring an ES
    Evolution Strategy
                              (1, )-ES
               One parent                 Number of offspring
         on each generation               (the only individuals that undergo selection)

         • The ES itself optimizes a quadratic function (sphere model)
         • ES efficiency: progress to the optimum on each generation
         • Fixed parameters:  = 10000,  = 1e-30, t = 240
         • Free parameters:  and c
         • Theoretical optima: (, c) = (5, 1) and (5, -1)

                                   Experimental settings
                                   60 search trials (ABRRHC)
                                   4 neighbors / configuration
                                   4 solutions to exchange among the AC agents
                                   proportion of AC agents / parameter to fine-tune = 2


D. Monett Díaz                   Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Obtained solutions
       Solution qualities for varying control parameters

                                                           Total of best singular solutions
                                                           (including all trials or repetitions) = 2 104

                                                                             Example of an “acceptable”
                                                                                    singular solution
                                                                               (according to its quality)
                                                                           (, c) = (5.15965245, 0.87023895)

           0,004                                                       5

                                                                       4
           0,003
                                                                       3
                                                             quality
 quality




           0,002
                                                                       2

           0,001                                                       1


              0
                                                                       0
               1,00   3,00   5,00      7,00       9,00                 -9,97   -7,15   -4,77   -2,42   -0,73   1,70   3,90   6,63    9,21
                              lambda                                                                      c

D. Monett Díaz                                Humboldt University of Berlin                             Doctoral dissertation, 25.02.2005
Pareto-optimal singular solutions
          60

          50

          40                                                                                  Weight vector
quality




          30                                                                            (w1, w2, w3) = (1.0, 0.0, 0.0)
          20

          10

           0                                                                   The importance goes to the solution qualities
               0    1000      2000       3000        4000      5000    6000
                                      time (msec)
                                                                                      (when calculating the worth)


          60

          50                                                                   Calculated Pareto-optimal solutions,
          40                                                                         according to all criteria,
quality




          30                                                                        are presented to the user
          20
                                                                                          (GUI + data files)
          10

           0
               0   5000    10000     15000   20000     25000   30000   35000
                                       func.eval.




  D. Monett Díaz                                               Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
Configuration process
 Estimated time per agent:
             AC 32,8282333 min
                                                  800
             AS 3,25705868 hours
                                                  600
 % from the total:
 Configurators        14,382467                   400
       Solvers        85,617533
                                                  200
    Total of algorithm’s
        evaluations     = 10 520                    0
                                                                ABRRHC              Solver
     (i.e. ES evaluations)
                                            time (min)        131,3129333         781,6940833


                                                 as all AC agents would             as all AS agents would
                                                     sequentially run                   sequentially run

                 Other experiments already done with ES and GA:
                     - varying the worth equation and the weight vectors
                     - varying the proportion of agents, exchanges, neighbors per solution, etc.
                     - studying IPs (e.g. HBIP, EIP, RequestIP) in detail

D. Monett Díaz                              Humboldt University of Berlin          Doctoral dissertation, 25.02.2005
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Ongoing projects
      • Optimizing risk strategies in random investment
        environments (GA, 4 free parameters, 2x agents)
          (E. Navarro, D. Monett, V. Uc Cetina: Machine Learning Approaches for Investment
           Strategies --- working paper)


      • Adapting soccer agents’ movement, RoboCup 3D
      Simulation League (ANN, 50 free parameters, 1x & 2x agents)
          New to +CARPS:
                 - Connection Clients or CC agents
                 - TCP/IP communication instead data files transfer
                 - related ontologies, classes, and agent behaviors and interactions


      • Including other configuration algorithms

D. Monett Díaz                         Humboldt University of Berlin       Doctoral dissertation, 25.02.2005
Outline

   • Introduction
         – Metaheuristics; Configuration process; Why agents?
         – PhD: Motivation – Goals - Contributions
   • Agent-Based Configuration of (Metaheuristic)
     Algorithms
         – Architecture; agents; interaction protocols; other details
   • Experimental results
   • Ongoing projects
   • Conclusions and Contributions


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Conclusions and Contributions

 +CARPS …
       is an agent-based approach to the configuration of algorithms
       including, but not limited to, metaheuristics

       is a framework that helps monitoring control factors of metaheuristics

       is a tool useful for conducting experiments when executing these
       algorithms

       follows a theoretical description and formalization of the configuration
       problem
           D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In
              Proceedings of the Workshop Concurrency, Specification, and Programming
              CS&P'2003, volume 2, Czarna, Poland.




D. Monett Díaz                          Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Conclusions and Contributions
 +CARPS …
       consists of different types of autonomous, cooperative agents that
       support the configuration of metaheuristic algorithms in a distributed
       fashion
       implements a Random-Restart Hill-Climbing algorithm to search for
       solutions during the configuration process
       includes new interaction protocols that are followed by the agents in
       order to cover communication requirements needed in the domain of
       analysis

       is a prototype implemented over JADE that follows FIPA specifications
       provides an infrastructure for a distributed, remote and parallel
       execution of configuration algorithms (i.e. ontologies, interaction
       protocols, agent behaviors, and other supporting classes)


D. Monett Díaz                   Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Agent-Based Configuration of
 (Metaheuristic) Algorithms
           - Doctoral dissertation -


      M.Sc. Dagmar Monett Díaz



   Artificial Intelligence, Computer Science Dept.
            Humboldt University of Berlin
                     25.02.2005
Extra, additional slides
- Used in answers, examples, discussion, etc. -



       M.Sc. Dagmar Monett Díaz



    Artificial Intelligence, Computer Science Dept.
             Humboldt University of Berlin
                      25.02.2005
“Fine-tuning” of metaheuristics
      Auto-adaptive metaheuristics…
      Brute force
           - Every configuration is tested and the best one is selected
      Meta-evolution as “configurator”
           - Evolutionary algorithms fine-tune other evolutionary algorithms
           - Examples:
                                  meta-level Evo.Alg.
                         GA         that produces                    ES+GA
                                    meta-solutions
  They optimize                                                                      It optimizes a
    benchmark                                                                        single case of
     functions           GA                                            GA           the sphere model


                 [Grefenstette, 1986]                              [Bäck, 1994]
                    [Pham, 1995]
                   [Pedroso, 1997]

D. Monett Díaz                     Humboldt University of Berlin             Doctoral dissertation, 25.02.2005
More general “configurators”
      Using experimental design
           - Statistical design + gradient descent [Coy et al., 2000] (two local search
             heuristics)
           - Fractional experimental design + local search [Adenso-Díaz and
             Laguna, 2003] (Tabu Search and Simulated Annealing)
      Racing algorithms
           - Sequentially evaluate candidate configurations and discard
             poor ones as soon as statistically sufficient evidence is gathered
             against them
           - Example:
                 F-Race: based on a statistical method for hypothesis testing
                 [Birattari et al., 2002] (MAX-MIN-Ant System)
      Parallel approaches
           - Independent runs: master-slave approach [Blesa and Xhafa, 2004]
            (Tabu Search)
           - Parallel implementations of metaheuristics have also contributed
             to the topic…
D. Monett Díaz                       Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Auto-adaptive metaheuristics
   Examples of mechanisms that are used:
       • Genetic operators encoded as part of the representation of individuals
          (e.g. ES, first Evo.Alg. that considered self-adaptation)
       • Variable population size / chromosomal length (e.g. GA)
       • Mutation schedules for varying the mutation of individuals at early
          and later evolution stages differently (e.g. GA)
             E.g.: non-uniform mutation
                                                        r : random in [0, 1]
                     v  (t ,UBk  vk ), r  0.5      T : max. generation number
              v 'k   k                                b : system parameter determining
                      vk   (t , vk  LBk ), r  0.5     the degree of dependency on
                                           (1 t )b                         iteration number t (e.g. b=5)
                     (t , y )  y  (1  r T )                           LB, UB: upper & lower bounds

       • Cooling schedules for controlling the search and the probability of
          accepting worsening solutions (e.g. SA)
                                                                                          t : temperature
              E.g.: geometric and arithmetic cooling
                                                                                          r : random in [0, 1]
                f ( s ) f ( s ')        t  t   ,  0                                 s, s’: solutions
              e        t          r ?   t  t   ,0    1                             f: solution quality

D. Monett Díaz                            Humboldt University of Berlin                 Doctoral dissertation, 25.02.2005
Constructing neighbor configurations
      AC agents construct neighbor configurations
      ISM agent manages the instantiation strategies
      Example: The new parameter value is randomly generated in a
               neighborhood of the current value, with
                                NeighborsPercent
                     sizeN 
                             index ( currentNei ghbor )
                 sizeN : size of the neighborhood
                 NeighborsPercent : percent of the original restriction to the free parameter
                 index(currentNeighbor) : number of neighbors already constructed + 1

       • As the number of neighbors increases, it decreases the ratio of the
         neighborhood around the current parameter value
       • Decreasing the neighborhood size adds lower noise to the current
         parameter value
       • Considering other adaptive techniques  local to the ISM agent !!

D. Monett Díaz                       Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Stop criteria
                     TS    1     TTenure=10                       1   TTenure=20           1    TTenure=50
                                       &                                    &                         &
                                 fixed factors                        fixed factors             fixed factors
                                           .                                .                          .
   NTrials = ??                            .
                                           .            +                   .
                                                                            .         +                .
                                                                                                       .
                     NTrials     TTenure=10             NTrials       TTenure=20      NTrials   TTenure=50
                                       &                                    &                         &
                                 fixed factors                        fixed factors             fixed factors



  • Metaheuristics are not exact methods
  • Goal: To find “acceptable” solutions beyond local optima
  • Challenge: In less trials as possible
  • One approach:
        Stop when the fluctuations of averaged solution qualities with the
        same configuration is not significant any more (-solution “stability”):
                                     i                 i 1

                                   s s
                                    j 1
                                               j
                                                       j 1
                                                              j

                  si  si 1                                                  NTrials = i+1
                                         i             i 1

D. Monett Díaz                       Humboldt University of Berlin                         Doctoral dissertation, 25.02.2005
Stability transition trial
     Other performance indexes that could be considered:
             - CPU time to get the solutions
             - number of function evaluations
     Related definitions:
                 -computational time “stability”
                 -evaluations “stability”
     However, it can happen that
                              NTrials for -solution “stability”
                                                    
                         NTrials for -computational time “stability”
                                                    
                             NTrials for -evaluations “stability”

     Stability transition trial Τ: Minimal number of trials at which the
     metaheuristic is solution, computational time and evaluations stable
     when evaluated with the same configuration

D. Monett Díaz                         Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Expectation value
  From Experimental Physics:
       • Given: N independent measurements of a physical constant
       • Mean of the measurements (“estimate” of the true value):
                                      N
                                  1
                             y
                                  N
                                    yi 1
                                             i
                                                                                      N
                                                                                  1
       • Expectation value or true value:                y  lim
                                                                           N    N
                                                                                    yi 1
                                                                                             i




  Expectation value of the solution qualities
       from the program outputs y(∙) when evaluating the metaheuristic
       with the same configuration, say p
                                                                T
                                                            1
       Correction:     E y ( p) s   y( p) s  lim          s       i
                                                                                  T: stability transition trial
                                                     T    T   i 1

       However, y(∙) may be not                   (and similarly for computational time, function
       normally distributed…                      evaluations or any other performance index…)

D. Monett Díaz                     Humboldt University of Berlin                       Doctoral dissertation, 25.02.2005
Expectation value
  Idea:
       what to expect from repeated outcomes
       • Discrete random variable X with values x1, x2, …, xN
       • Probability function f(xi)=P(X= xi) (probability of obtaining each xi)
       • Expectation value:                         Special case: If f ( x ) 
                                                                               1
                                  N                                                                    i
                                                                                                                N
                        E X    xi  f ( xi )                      then                  N
                                                                                        1
                                  i 1
                                                                             E X       x       i       X
                                                                                        N   i 1

  Expectation value of the solution qualities
       from the program outputs when evaluating the metaheuristic
       with the same configuration
                                                    (and similarly for computational time, function
                                                    evaluations or any other performance index…)
       • Probability function is unknown
       • It can be estimated after the trials are done, i.e. by simulation
       • Like this: Relative frequencies could be tabulated. They are estimates
         of the probabilities
                                                              1
                                        Correction: f ( si )  is difficult to have…
                                                                             T
D. Monett Díaz                        Humboldt University of Berlin                Doctoral dissertation, 25.02.2005
Goal of the experiments: Twofold
  Testing the system
         How to use +CARPS: conducting experiments & monitoring the
        configuration process
            • Aim: to seek for optimum values to the user problem’s
            parameters
            • User problem: Chemical optimization problem (Parameter estimation
              in chemical reactions)
            • Former obtained solutions are reproduced; better ones are found

  Evaluating the system
         How does +CARPS work: functionality of the system
           • Aim: to seek for optimum values to the algorithm’s parameters
           • Configuration algorithm is tested
           • Also communication among the agents, interaction protocols, etc.
           • User problem: of secondary importance at this level

D. Monett Díaz                  Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Technical information
     +CARPS (by August 2004)
     Agent framework:                 JADE 3.2
     Programming language:            JavaTM 2 SDK Standard Ed. 1.4.2
     Development tool:                BlueJ 2.0 beta
     Source code lines:                22 500
     (most important) Classes         113 (.java)

     GAs (by 2002)
     Programming language:            Visual C++ 5.0
     Development tool:                Microsoft Developer Studio 97
     Source code lines:                6 000
     Total of classes:                10 (.cpp) + 10 headers (.hpp)

D. Monett Díaz               Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Agent-based configuration



        Optimization        Simulation                   Optimization




         Agent-based         Algorithm                    Algorithm
         configuration       execution                   functioning


D. Monett Díaz           Humboldt University of Berlin    Doctoral dissertation, 25.02.2005
+CARPS : Development

• software development: it was carried out in an evolutionary,
  object-oriented fashion
• bottom-up strategy: behaviors and agent actions and objects
  were designed, implemented, debugged, and tested as the
  needs arose, from simple to more complex components.
  For example, a draft for the configuration ontology was first
  considered which later turned into the four vocabularies and
  ontologies
• GUI: it was very important for testing agent interactions and
  their functioning
• JADE (Java Agent DEvelopment Framework)
      – FIPA specifications
• +CARPS classes are Java classes


D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions


      Real problem




                     user problems



   Motivation:                                          (metaheuristic) algorithms
    Powerful algorithms are needed to
     solve several real problems
                                                        Developer
    Configuring them can be a very
     difficult combinatorial problem                                      System


D. Monett Díaz               Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions


      Real problem




                      user problems



   Goals:                                                (metaheuristic) algorithms
    To support the configuration
     process of these algorithms
                                               Developer
     . Autonomously, distributed, remote, etc.
    To (semi)automate both monitoring and                                 System
     fine-tuning of parameters and conducting
     experiments
D. Monett Díaz                Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
Motivation – Goals – Contributions


      Real problem


                                                                            +CARPS
                      user problems



   Contributions:
                                                          (metaheuristic) algorithms
    +CARPS, agent-based approach
     for configuring
    . Distributed, collaborative problem solving Developer
    . Necessary information specialization/processing                       System
    . Flexibility: important

D. Monett Díaz                 Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
Agent-based Configuration: Steps

      I: Initializationof variables to manage solutions, agent
         Initialization of variables to manage solutions, agent
      communications, and conditions for stop criteria. Initialization of the
      search procedure.

      II: Constructionof starting configurations with initial levels for for the
          Construction of starting configurations with initial levels the
      free parameters.

      III: Agent-based configuration: application the search procedure
           Agent-based configuration: application of of the search procedure
      and exchange of best-so-far solutions among the agents, until stop
      criteria meet.

      IV: Organization of partialand global solutions. Report best-so-far
                          partial and global solutions. Report best-so-far
      configuration and Pareto-optimal solutions.



D. Monett Díaz                    Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Why agents for configuring?
  Distributed execution
  The (metaheuristic) algorithms and the agents could be physically
  distributed over a network.
  Remote execution
  Local agents can interact with other agents situated on remote computers,
  thus allowing for the remote execution of algorithms, which do not need
  to be located where the users are.
  Cooperation
  Agents can decide whether to cooperate or not, as well as to ask for
  Cooperation, if needed, in order to solve the original configuration
  problem. Furthermore, they can cooperate by exchanging best-so-far
  obtained solutions.
  Autonomy & specialization
  Agents that interact with the users do not need to know how to configure
  algorithms, nor to solve them or to manage solutions (and vice versa),
  for example, in order to operate and to have control over their actions.

D. Monett Díaz                 Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Why agents …

  Exploration of the search space
  Subproblems are considered so as to cover the complete search space as
  well as possible.

  Exploitation of the search space
  It is done by studying a free parameter in detail. The more different
  parameter variations are considered, the wider the analysis and study of
  the related parameter.

  Incremental quality solution
  Solutions are improved by each agent when applying the configuration
  algorithm and solutions received from other agents can also improve the
  ones obtained so far.



D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Fine-tuning (II)
   Example by using GA
          Factor in study: Population size, PopSize
          Levels: 50, 100, 150 (3 configurations)
          Number of runs for each configuration: NRuns

            1 PopSize=50               1 PopSize=100                   1 PopSize=150
                       +                           +                              +
                 fixed factors               fixed factors                  fixed factors
                       .                             .                              .
                       .                             .                              .
                       .                             .                              .
      NRuns PopSize=50            NRuns PopSize=100                 NRuns PopSize=150
                       +                           +                              +
                 fixed factors               fixed factors                  fixed factors



                                  (Factori * Levelj * Runsk )

                                        Which is the “most acceptable” solution?
                                        Which is the “best-so-far” configuration?
D. Monett Díaz                      Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Fine-tuning (III)
       tunable parameters (or factors) = controllable parameters
                            = free parameters


            parameter tuning = fine-tuning = parameter setting
                  = configuring = configuration process


 Configuration
  a specific setting or combination of free and fixed parameters

 Restrictions
  define the levels or different values that parameters may have

D. Monett Díaz                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Worth of a metaheuristic

  Pareto-optimum: Situation where it is not possible to improve (to decrease)
                               the value of an objective function without deteriorating
                               (increasing) that of at least one other


  Example of worth equation (using the weighted sum approach)
                      worth(C )  w1   s2  w2   t2  w3   v2
             where:                                    m                                 std. deviations
                        m

                         x  xi 2                 x
                                                      i 1
                                                             i         Normalization (e.g.):
                  2
                 x    i 1           ,      x                                      2
                                                                                     x
   variances                   m    averages
                                                        m
                                                                       max(  s2 ,  t2 ,  v2 )


D. Monett Díaz                         Humboldt University of Berlin         Doctoral dissertation, 25.02.2005
Agent-based Config.: Algorithm
   Configuration ABRRHC(c, p) {
     // c : initial configuration from SCB agent
     // p : index of the parameter to fine-tune
    bestConfiguration = c;    i = j = k = 0;
    do while (i < MaxExchanges and condition1) {
       do while (j < MaxTrials and condition2) {
          do while (k < MaxNeighbors and condition3) {
             nc = neighborConfiguration(c, p);
             evaluate(nc);
             if quality(nc) ≤ quality(bestConfiguration) then
                bestConfiguration = nc;       k++; }
          c = bestNeighbor();
          if isRestartAllowed then {
             rc = restartConfiguration();
             evaluate(rc);
             if quality(rc) ≤ quality(bestConfiguration) then
                bestConfiguration = rc;
             c = rc; }       j++; }
       if isExchangeAllowed then {
          ec = exchangeSolution(bestSolution);
          if quality(ec) ≤ quality(bestConfiguration) then
             bestConfiguration = ec;
          c = ec; }       i++; }
    return bestConfiguration; } // end ABRRHC
+CARPS : Development & tools


 • JADE (Java Agent DEvelopment Framework)
 • +CARPS classes are Java classes.
 • +CARPS packages enclose agents,
   ontologies, and utilities, separately.
 • Classes & hierarchies will be presented as
   they are showed when using the BlueJ Java
   development tool.


D. Monett Díaz    Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
+CARPS’ Graphical User Interface
Config vocabulary and ontology




D. Monett Díaz   Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
InstStrategy vocabulary and ontology




D. Monett Díaz   Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
+CARPS Agents

       Types of agents
                 UM    User Mediators
                 ISM   Instantiation Strategy Managers
                 SCB   Starting Configuration Builders
                 AC    Algorithm Configurators
                 AS    Algorithm Solvers
                 SM    Solution Managers

     - Agent communication: relevant
     - Specialization and distributed information processing: relevant
     - Following the standards (e.g. FIPA specifications): important



D. Monett Díaz             Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
SCB agent : Example
                     P  D1 , P2 , P3
                      1

                                                                  s1b       randInit ,     
                                                                   b                        
                                                                   s2       lowLevelInit , 
                          SCB1                              ST1   b                        
                                                                  s3        uppLevelInit ,
                                                                  s b       avgInit        
                                                                   4                        
                      level11 , level12 ,
                      level13 , level14



       C1 : level11 , P2 , P3                        C3 : level13 , P2 , P3 

                            C 2 : level12 , P2 , P3                         C 4 : level14 , P2 , P3 

D. Monett Díaz                              Humboldt University of Berlin             Doctoral dissertation, 25.02.2005
Algorithm Configurators: IPs

        AC & AS agents help UM agents with the
                configuration process

                   UM agents book their services

     • HBIP : Helper-Booker Interaction Protocol
           – HB Initiator Finite State Machine (AC & AS)
           – HP Responder Finite State Machine (UM)
                 • HelperBookerProtoASResponder.java
                 • HelperBookerProtoACResponder.java

D. Monett Díaz              Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Helper-Booker-Protocol

                                 AS, AC                                            UM
                     Initiator                                       Participant

                                          propose
                                 reject-proposal

                                     failure

                                 accept-proposal

                                     inform

                                                 failure
                          [dead-
                          line1]

                                                 confirm

                                   cancel                [deadline2]




D. Monett Díaz                       Humboldt University of Berlin                 Doctoral dissertation, 25.02.2005
Helper-Booker-Initiator-FSM


             SEARCH_DF            DELAY


                              1

  VERIFY_REGISTERED_AGENTS            REMOVE_BOOKER

                2             4                               12


            SND_PROPOSAL          7       SND_CANCEL

                         3
                                                     10
                                          6
                        RCV_RESPONSE           RCV_DATA
                    5                                         8
                                                          9


                                      SND_RES_NOTIFICATION

                                                11


             TERMINATE_PROTOCOL           MAKE_CLONE
Helper-Booker-Responder-FSM

                   RCV_PROPOSAL               1

                         2

            VERIFY_BOOKED_AGENTS
               4                    3                             13

 SND_REJECT_PROPOSAL         SND_ACCEPT_PROPOSAL
                                              5
                                                         8
                   6                    SND_DATA
                                              7

                                RCV_RESPONSE                 10

                                9        11         12

                INCREASE_BOOKERS                  SND_CANCEL



                              VERIFY_TERMINATION
                                              14

                              TERMINATE_PROTOCOL
Agents’ communications
    Most relevant IPs in +CARPS                                    Engagement-Protocol



                                                                                    AC                                 AS
         Interaction Protocols                                          Initiator                        Participant

                                                                                          query-if
                             Helper-Booker IP
                                                                                         refuse

                                 Engagement IP                                           inform

                                                                            [deadline1]
                                  Request IP                                                      confirm

                                                                                                  [deadline2]
                                                                                     cancel


  - Which agents will solve the algorithms?
  - Among AC agents (configurators)
    and AS agents (solvers)
  - Particular case of the standard FIPA Query IP
  - Configurators are initiators of the EIPs
  - Solvers are responders

D. Monett Díaz                          Humboldt University of Berlin                         Doctoral dissertation, 25.02.2005
Algorithm Configurators: IPs
     An engagement is a compromise, a contract,
     between two parts

                  AS agents solve the algorithm
                       being configured

                 Engagement Interaction Protocol

                 AC agents need AS agents in the
                     configuration process

D. Monett Díaz             Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Algorithm Configurators: IPs
      • HBIP : Helper-Booker Interaction Protocol
            – HB Initiator Finite State Machine (AC & AS)
                 • HelperBookerProtoInitiator.java
            – HP Responder Finite State Machine (UM)
      • EIP : Engagement Interaction Protocol
            – E Initiator Finite State Machine (AC)
                 • EngagementProtoInitiator.java
            – E Responder Finite State Machine (AS)
      • Request Interaction Protocol
            – Request Initiator Finite State Machine (AC)
                 • RequestProtoInitiator.java
D. Monett Díaz              Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Algorithm Configurators: IPs
                 Engagement-Protocol


                                AC                                                 AS
                    Initiator                                        Participant

                                       query-if

                                     refuse


                                     inform

                         [deadline1]               confirm


                                 cancel                 [deadline2]




D. Monett Díaz                       Humboldt University of Berlin                 Doctoral dissertation, 25.02.2005
Algorithm Configurators: IPs
            Engagement-Initiator-FSM


                         SELECT_SOLVER



                                                2
                        SND_QUERY_IF                        REMOVE_SOLVER

                           1                4
                                                                   6

                                              5
                        RCV_RESPONSE                   SND_RES_NOTIFICATION
                    3
                                                                   7


                                                        TERMINATE_PROTOCOL




D. Monett Díaz                    Humboldt University of Berlin         Doctoral dissertation, 25.02.2005
Engagement-Responder-FSM

                                                              1
                                RCV_QUERY_IF
                                         2

                          VERIFY_CONDITIONS
                                                                          10
                            4                       3

                     SND_REFUSE              SND_INFORM
                                                        5

                                6            RCV_RESPONSE             7

                                             8                    9

                                    ENGAGE                  SND_CANCEL



                                        VERIFY_TERMINATION
                                                        11

                                       TERMINATE_PROTOCOL




D. Monett Díaz                       Humboldt University of Berlin             Doctoral dissertation, 25.02.2005
Agents’ communications
    Most relevant IPs in +CARPS                                     FIPA-Request-Protocol


                                                                        Initiator                    Participant

         Interaction Protocols                                                          request

                                                                                        refuse
                             Helper-Booker IP                                          [refused]

                                                                                    agree
                                                                                [agreed and
                                 Engagement IP                                  notification necessary]


                                  Request IP
                                                                                      failure

                                                                                                     [agreed]
                                                                              inform-result: inform
  - Solving the algorithms
  - Among AC agents (configurators)
    and AS agents (solvers)
  - FIPA Request IP - like
  - Configurators are initiators of the EIPs
  - Solvers are responders

D. Monett Díaz                          Humboldt University of Berlin                   Doctoral dissertation, 25.02.2005
Agents’ communications
                 Request-Initiator-FSM


                                                   SND_REQUEST
                                                                            8


                                    1            RCV_RESPONSE

                                               3                        2
                                                           5
                        RCV_RES_NOTIFICATION                        UPDATE_CONTENT
                    4              6                       7                9

                              RCV_DATA                          TERMINATE_PROTOCOL




D. Monett Díaz                          Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
Solver-FSM


                        PREPARE_DATA



                                                      1
                      VERIFY_REPETITIONS                               EXEC_ALGORITHM

                                5                                       4                     3

                                                                                 2
                       VERIFY_SOLUTIONS          UPDATE_COUNTER

                        6
                                          7
                                                                       PROCESS_RESULTS
                 CONSTRUCT_SOLUTIONS


                            UPDATE_VARIABLES
                                                                       PROCESS_ERR_CODE



                       TERMINATE_SOLVER




D. Monett Díaz                         Humboldt University of Berlin             Doctoral dissertation, 25.02.2005
AC-AS communications

                              paths to M and io files,
                              par. names, ag. lists
                                  data

                 configs
     AC                      AS             M
                 solutions
                                  results
                                                                    input files


                                         config
                              AC                       AS                               M        .exe
                                       singular
                                       solution

                                                                    output files
D. Monett Díaz                      Humboldt University of Berlin            Doctoral dissertation, 25.02.2005
+CARPS : Tree
                 +carps
                      src
                            agent
                                    AC
                                         behaviours
                                    AS
                                    ISM
                                                              Agents and their behaviors
                                    SCB
                                    SM
                                    UM
                            metah
                                 copga           Algorithms to configure
                                 evoes
                            ontology
                                 agentList
                                 configuration          Vocabularies and ontologies
                                 strategy
                                 userproblem
                            util
                                 comm
                                 graphics
                                 gui              Utilities
                                 io
                                 jExtra
                                 table
D. Monett Díaz                                Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
<time.h>


 GA               <windows.h>
                                              time.hpp            time.cpp


                                            commonDef.hpp
                    <math.h>

                                              random.hpp          random.cpp
                  <assert.h>


                 <fstream.h>                 utility.hpp         utility.cpp


                 <iostream.h>                 gene.hpp            gene.gpp


                  <iomanip.h>              chromosome.hpp       chromosome.cpp



                                             objfunc.hpp         objfunc.cpp



                                           population.hpp       population.cpp



                                                 ga.hpp              ga.cpp
                    <ctype.h>

                   <stdlib.h>
                                                copga.hpp          copga.cpp
                   <string.h>

                                              mainGA.cpp

D. Monett Díaz                  Humboldt University of Berlin   Doctoral dissertation, 25.02.2005
Agents and containers
                                    AC1         AC2


                               DF         AMS     RMA
    UM1          SM1

          ISM1         SCB1         Main container             AS1        AS2


        Container 1                                            Container 2
    Platform 1


                                    Network
                                                        UM2        ISM2     SM2     SCB2
          AS3      AS4

                                                              DF       AMS      RMA
     DF         AMS      RMA

                                                                   Main container
          Main container
                                                        Platform 3
   Platform 2
JADE: Sniffer agent
Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation
Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation
ES: c vs. quality
           theoretical value for the optimum: c = 1 and c = -1
           60                                                                                  5

           50                                                                                  4
           40
                                                                                               3




                                                                                    quality
quality




           30
                                                                                               2
           20
                                                                                               1
           10

           0                                                                                   0
           -9,97    -7,15    -4,77   -2,42    -0,73   1,70   3,90    6,63   9,21               -9,97   -7,15   -4,77   -2,42   -0,73    1,70    3,90          6,63   9,21
                                                 c                                                                                c

            1                                                                                  0,004

           0,8                                                                                 0,003




                                                                                     quality
           0,6
 quality




                                                                                               0,002
           0,4
                                                                                               0,001
           0,2
                                                                                                   0
            0                                                                                      -10,0 -8,00 -6,00 -4,00 -2,00 0,00   2,00   4,00    6,00     8,00 10,00
            -9,97    -7,15   -4,77    -2,42   -0,73   1,70   3,90    6,63    9,21                    0
                                                  c                                                                                c




   D. Monett Díaz                                                   Humboldt University of Berlin                               Doctoral dissertation, 25.02.2005
1prop&4n
              10
               8
               6
                4
                2
                0
c




               -2
               -4                                                                          9,0E+03
               -6                                                                          8,0E+03
               -8                                                                          7,0E+03
              -10




                                                                             time (msec)
                                                                                           6,0E+03
                    0       2         4             6        8          10                 5,0E+03
                                          lambda                                           4,0E+03
                                                                                           3,0E+03
              9,0E+04                                                                      2,0E+03

              8,0E+04                                                                      1,0E+03

              7,0E+04                                                                      0,0E+00
              6,0E+04                                                                                0   5   10   15    20    25    30    35    40     45
func. eval.




              5,0E+04                                                                                                    quality
              4,0E+04
              3,0E+04
              2,0E+04
              1,0E+04
              0,0E+00
                        0   5   10   15    20      25   30   35   40    45
                                            quality




   D. Monett Díaz                                             Humboldt University of Berlin                            Doctoral dissertation, 25.02.2005
Testing engagements
Elapsed time of the Engagement IPs
                     - Proportion of AC agents per parameter to fine-tune = 2
                     - Intra-platform communication
                     - AC agents run in a main container; AS agents, in a secondary one
                     - Number of parameter to fine-tune = 2 ( and c)

                     25

                     20

                                                                             4 AC agents & 4 AS agents
        time (sec)




                     15

                     10
                                                                                  Engagements:
                      5                                                           AC1-AS2 (or AC1-AS4)
                                                                                  AC2-AS3
                      0
                            1        2        3             4                     AC3-AS1
                     AC   0,094    11,688   23,359       0,094                    AC4-AS4 (or AC1-AS2)
                     AS   23,875   0,359    12,11        0,344



D. Monett Díaz                               Humboldt University of Berlin            Doctoral dissertation, 25.02.2005
Pareto-optimal singular solutions
          60

          50

          40
                                                                                               Weight vector
                                                                                         (w1, w2, w3) = (1.0, 0.0, 0.0)
quality




          30

          20

          10

           0
                                                                                The importance goes to the solution qualities
               0    1000      2000       3000        4000      5000     6000           (when calculating the worth)
                                      time (msec)

          60

          50

          40                                                                     Four best Pareto-optimal singular
                                                                                solutions according to their qualities
quality




          30

          20

          10
                                                                                       lambda          c              quality
           0
                                                                                     5,15965245    0,87023895          3,98E-05
               0   5000    10000     15000   20000     25000   30000   35000         4,86909981   -0,98344576          5,70E-05
                                       func.eval.
                                                                                      4,4049138   -1,00838506          3,43E-04
                                                                                     4,83154784   -1,04477195          3,57E-04
               Total of Pareto-optimal
                                                               = 388
               singular solutions

  D. Monett Díaz                                                Humboldt University of Berlin           Doctoral dissertation, 25.02.2005
(cont.)
              1,2E+04

              1,0E+04
time (msec)




              8,0E+03

              6,0E+03

              4,0E+03

              2,0E+03                                                               4,0E+04
                                                                                    3,5E+04
              0,0E+00
                               1                    2                               3,0E+04




                                                                      time (msec)
                   AC         10985                 94                              2,5E+04
                   AS         11266                 141                             2,0E+04
                                                                                    1,5E+04
                                                                                    1,0E+04
              6,0E+04                                                               5,0E+03
                                                                                    0,0E+00
              5,0E+04                                                                          1       2                3             4
                                                                                               109    35843           11000         11015
time (msec)




              4,0E+04                                                                    AC
                                                                                         AS   11375    203            11422         36172
              3,0E+04

              2,0E+04

              1,0E+04

              0,0E+00
                        1      2       3      4     5          6

                   AC   156   57953   13500   125   93        156
                   AS   437   14687    469    438   469      59328




    D. Monett Díaz                                      Humboldt University of Berlin                        Doctoral dissertation, 25.02.2005
(cont.)
                14
                12
                10
 best quality




                 8
                 6
                 4
                 2
                 0                                                                                                       2,50E-03                                                        4,00E-03
                     1        30        59        88        117        146        175    204    233   262                                                                                3,50E-03
                                                                                                                         2,00E-03
                                                                   t                                                                                                                     3,00E-03
                                                                                                                         1,50E-03




                                                                                                            best worth
                                                                                                                                                                                         2,50E-03
                          8n            6n         4n         2n
                                                                                                                         1,00E-03                                                        2,00E-03
                                                                                                                                                                                         1,50E-03
                                                                                                                         5,00E-04
                                                                                                                                                                                         1,00E-03
                                                                                                                         0,00E+00                                                        5,00E-04
                5,0E-04
                                                                                                                         -5,00E-04                                                       0,00E+00
                4,0E-04                                                                                                              1   30    59   88   117 146     175 204   233 262
 best quality




                3,0E-04                                                                                                                                     t

                                                                                                                                          2n        4n     8n        6n
                2,0E-04

                1,0E-04

                0,0E+00
                          1        30        59        88     117           146    175    204   233   262
                                                                        t
                                   4n         8n




D. Monett Díaz                                                                            Humboldt University of Berlin                                         Doctoral dissertation, 25.02.2005
q9vp
                1,8


                1,6
r2




                1,4


                1,2
                                                                                                                  15
                 1                                                                                                12




                                                                                                   best fitness
                      2                 2,5                  3              3,5              4
                                                                                                                  9
                                                             r1
                                                                                                                  6

                                                                                                                  3

                 0,09                                                                                             0
                0,088                                                                                                  1   10   19   28   37        46   55   64   73   82    91   100
                0,086                                                                                                                           population
best distance




                0,084
                0,082                                                                                                      current    best-so-far
                 0,08
                0,078
                0,076
                0,074
                          1   10   19         28   37    46       55   64   73     82   91   100
                                                        population

                              current          best-so-far




    D. Monett Díaz                                                                Humboldt University of Berlin                                     Doctoral dissertation, 25.02.2005
2n&repeat

              1,4E+06
              1,2E+06

              1,0E+06
time (msec)




              8,0E+05

              6,0E+05
              4,0E+05
              2,0E+05
              0,0E+00
                         2n-1     2n-2     2n-3      2n-4     2n-5     mean

              Solver    911399   798396   1136179   982935   992848   964351,4
              ABRRHC    99601    87823    108009    112955   103573   102392,2
                                                                                         1,4E+06
                                                                                         1,2E+06
                                                                                         1,0E+06



                                                                           time (msec)
                                                                                         8,0E+05
                                                                                         6,0E+05
                                                                                         4,0E+05
                                                                                         2,0E+05
                                                                                         0,0E+00
                                                                                                    2n-1     2n-2     2n-3       2n-4       2n-5
                                                                                         Solver    911399   798396   1136179    982935     992848
                                                                                         ABRRHC    99601    87823    108009     112955     103573




    D. Monett Díaz                                              Humboldt University of Berlin                          Doctoral dissertation, 25.02.2005
(cont.)
                2,5E+07

                2,0E+07
  time (msec)




                1,5E+07

                1,0E+07

                5,0E+06

                0,0E+00
                            1x        2x            3x

                Solver    2414233   7422779      16841628
                ABRRHC    185971    1141846       3319888
                                                                              60

                                                                              50

                                                                              40




                                                            time (min)
                                                                              30

                                                                              20

                                                                              10

                                                                                  0
                                                                                          1x            2x                3x

                                                                         Solver       20,11860833   30,92824583        46,7823
                                                                         ABRRHC       1,549758333   4,757691667      9,221911111




D. Monett Díaz                                Humboldt University of Berlin                            Doctoral dissertation, 25.02.2005
Related publications
   D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged
      for Configuring. In G. Lindemann, H.-D. Burkhard, L. Czaja, A. Skowron, H.
      Schlingloff, Z. Suraj, editors, Proceedings of the Workshop Concurrency,
      Specification, and Programming CS&P'2004, volume 3: Multiagent Systems and
      Applications, pages 507-518, Caputh, Germany. Informatik-Bericht Nr. 170.
      (Also in Special Issue of Fundamenta Informaticae –to appear–)
   D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In D.
      Khadraoui, editor, Proceedings of the International Conference on Advances in
      Intelligent Systems - Theory and Applications, AISTA'2004, IEEE Computer Society,
      University of Canberra and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg.
   D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In Ch.
      Blum, A. Roli, M. Sampels, editors, Proceedings of the First International Workshop on
      Hybrid Metaheuristics, HM'2004, at the 16th European Conference on Artificial
      Intelligence, ECAI'2004, pages 115-125, Valencia, Spain.
  D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An
     Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul.,
     11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany.
  D. Monett (2001). On the automation of evolutionary techniques and their application to
     inverse problems from Chemical Kinetics. In C. Ryan, editor, Proceedings of the
     GECCO'01 Graduate Student Workshop, Genetic and Evolutionary Computation
     Conference, pages 429-432, San Francisco, California, USA.
  D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In L.
     Czaja, editor, Proceedings of the Workshop Concurrency, Specification, and
     Programming CS&P'2003, volume 2, pages 353-364, Czarna, Poland. Zakład Graficzny
     UW, zam. 591/2003.
D. Monett Díaz                        Humboldt University of Berlin       Doctoral dissertation, 25.02.2005
Conclusions and Contributions
    +CARPS, agent-based approach to the configuration of algorithms
    including, but not limited to, metaheuristics is proposed

    +CARPS consists of different types of cooperative agents that support the
    autonomous configuration of metaheuristic algorithms and that follows
    FIPA specifications in a distributed fashion

    Ontologies, interaction protocols, agent behaviors, and other supporting
    classes conform the +CARPS infrastructure for the agent-based
    configuration

    Conception and development of new interaction protocols that are
    followed by the agents in order to cover communication requirements
                                                        +CARPS
    needed in the domain of analysis                                 I/O Layer
                                                                 Algorithm
                                                                Configuration
                                                                   Layer           Algorithm
                                                                                    Solution
                                                                                     Layer


                                                                          Communication Layer


D. Monett Díaz                  Humboldt University of Berlin        Doctoral dissertation, 25.02.2005
Conclusions and Contributions
    Theoretical description and formalization of the configuration problem
        D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In
           Proceedings of the Workshop Concurrency, Specification, and Programming
           CS&P'2003, volume 2, Czarna, Poland.
    Implementation of a Random-Restart Hill-Climbing algorithm that some
    specialized agents apply to search for solutions during the configuration
    process

    +CARPS is also a framework in which monitoring of control factors of
    metaheuristics can be easily made

    At the same time, it can be seen as a powerful tool useful for conducting
    experiments when executing metaheuristic algorithms
                                                                           +CARPS

                                                                                                  I/O Layer
                                                                           Algorithm
                                                                          Configuration
                                                                             Layer           Algorithm
                                                                                              Solution
                                                                                               Layer


                                                                                    Communication Layer


D. Monett Díaz                          Humboldt University of Berlin          Doctoral dissertation, 25.02.2005

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Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation

  • 1. Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation - M.Sc. Dagmar Monett Díaz Artificial Intelligence, Computer Science Dept. Humboldt University of Berlin 25.02.2005
  • 2. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 3. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 4. Metaheuristics A metaheuristic is “a master strategy that guides and modifies other heuristics (like local search procedures) to produce solutions beyond those that are normally generated in a quest for local optimality“ [Laguna 2002] Examples of metaheuristics: - Traditional approaches: •EC (Evolutionary Computation): GA (Genetic Algorithms), ES (Evolution Strategies), GP (Genetic Programming), etc. •SA (Simulated Annealing), TS (Tabu Search), ANN (Artificial Neural Networks), EDA (Estimation of Distribution Algorithms), ACS (Ant Colony Systems), etc. - Hybrid metaheuristics  recent approaches! New research goals in metaheuristics domain : - To combine aspects from different metaheuristics, Artificial Intelligence, Operations Research techniques, etc. - Experimental design for configuring is important - Optimization of parameters (i.e. configuration process) is a relevant issue D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 5. Configuration of algorithms (or “fine-tuning” of algorithms) Shortcomings: Not all metaheuristic algorithms are auto-adaptive (in particular the hybrid approaches) Usually, control parameters are set by hand or in the spirit of brute-force mechanisms; time-consuming task Very few published research works; not yet an established research area Distributed, remote or parallel execution of configuration algorithms: not existing Special topic in most recent conferences and workshops (e.g. HM’04 at ECAI’04 and PSGEA’05 at GECCO’05); current open question!! D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 6. Example: a Tabu Search Algorithm • Fixed parameters: - Tabu criterion (what is forbidden for a given number of iterations) - Nr. of elite solutions (intensification strategy: restarting the search around them) - Stop criterion (e.g. maximum number of iterations) • Free parameter (i.e. factor in study): Tabu list length or tabu tenure - Levels: 10, 20, 50  3 configurations • Number of global runs (without varying any parameter): NTrials one TS 1 TTenure=10 1 TTenure=20 1 TTenure=50 run!! & & & fixed factors fixed factors fixed factors . . . . + . + . . . . NTrials TTenure=10 NTrials TTenure=20 NTrials TTenure=50 & & & fixed factors fixed factors fixed factors ► Which is the “most acceptable” configuration? D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 7. Why agents for configuring? Could agents substitute humans in their tasks? Could they behave as experts would do? E.g. • by alleviating the time required while configuring, i.e., the cost of time-intensive fine-tuning? • by entering data and processing results? • by autonomously conducting the required experiments? • by (semi) automating the configuration process? Answer: Agent-based configuration !! Multiagent Systems: properties that are of interest - Distributed / Remote execution - Cooperation among the agents - Autonomy & Specialization - Collaborative exploration / exploitation of the search space D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 8. Motivation – Goals – Contributions user problems Motivation:  Powerful algorithms are needed to (metaheuristic) algorithms solve several real problems  Configuring them can be a very difficult combinatorial problem D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 9. Motivation – Goals – Contributions user problems Goals:  To support the configuration (metaheuristic) algorithms process of these algorithms . Autonomously, distributed, remote, etc.  To (semi)automate both monitoring and fine-tuning of parameters and conducting experiments D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 10. Motivation – Goals – Contributions user problems +CARPS Contributions:  +CARPS, agent-based approach (metaheuristic) algorithms for configuring . Distributed, collaborative problem solving . Necessary information specialization/processing . Flexibility: very important D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 11. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 12. +CARPS : Architecture +CARPS : Multi-Agent System for Configuring Algorithms in Real Problem Solving +CARPS I/O Layer Algorithm Configuration Layer Algorithm Solution Layer Communication Layer D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In Proceedings of the International Conference on Advances in Intelligent Systems – Theory and Applications, AISTA'2004, IEEE Computer Society, University of Canberra and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg. D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In Proceedings of the First International Workshop on Hybrid Metaheuristics, HM'2004, at the 16th European Conference on Artificial Intelligence, ECAI'2004, Valencia, Spain. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 13. +CARPS : Agent interactions Pf , Px , ag. lists GUI MAS-User UM interaction Pf , ag. lists best ag. lists solutions paths to M ABRRHC and i/o files info and SM par. names, ag. lists ag. lists exch. solutions solutions data configs configs M solutions AC AS solutions TS, GA, ES, etc. STi initial results configs Algorithm’s execution: optimization process STi (user problem’s parameters) ISM SCB Configuration process: optimization process (metaheuristic’s parameters) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 14. Agent-Based Configuration Algorithm Configuration algorithm: Agent-Based Random-Restart Hill-Climbing Particularities: • Stochastic Hill-Climbing algorithm (restart from different candidate solutions) • Well-known metaheuristic; easy to implement • Construction of a topology (neighbors per solution) • Migration policy (AC agents exchange best-so-far obtained solutions) • ABRRHC: itself a hybrid metaheuristic Implementing a different only few changes in configuration algorithm specialized agent!!! D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 15. Worth of a metaheuristic Factors used to analyze metaheuristic’s performance: • quality of the best solution found until a stop criterion is verified • time to get the best solution • algorithm's time to reach an “acceptable“ solution • number of function evaluations performed until a stop criterion is verified, etc. Best-so-far configuration • a worth equation indicates “how good” the metaheuristic is • more than one factor at a time is considered • minimization procedure Example of worth equation (using a weighted sum approach) worth( p )  w1  f s  w2  f t  w3  f v Consider quality of the solutions, time to get fx them and evaluations & Normalization, e.g.: for all repetitions with the max( f s , f t , f v ) same configuration (i.e. p) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 16. Agents’ communications Interaction Protocols (IPs) • Define typical patterns of message exchange between agents • Agents are supposed to behave consistently by following such IPs • FIPA standards (e.g. Contract Net IP) • New applications might need new IPs – E.g. agent-based configuration of (metaheuristic) algorithms • In +CARPS: IPs are implemented as FSM Behaviors from JADE • Agent communications: ACL messages, FIPA compliant D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged for Configuring. In Proceedings of the Workshop Concurrency, Specification, and Programming CS&P'2004, volume 3: Multiagent Systems and Applications, Caputh, Germany (Also in Special Issue of Fundamenta Informaticae –to appear–) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 17. Agents’ communications Most relevant IPs in +CARPS Helper-Booker-Protocol AS, AC UM Initiator Participant Interaction Protocols propose reject-proposal Helper-Booker IP failure accept-proposal Engagement IP inform failure [dead- Request IP line1] confirm cancel [deadline2] - Which agents will participate? - Among UM agent (booker) and AC and AS agents (helpers) - UM registers with the Directory Facilitator - Helpers are initiators of the HBIPs; bookers are responders D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 23. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 24. Metaheuristics that are considered Genetic Algorithm (GA) • GA optimal parameters  unknown • Parameter estimation in chemical reactions (copolymerizations) D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul., 11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany. D. Monett (2001). On the automation of evolutionary techniques and their application to inverse problems from Chemical Kinetics. In Proceedings of the GECCO'01 Graduate Student Workshop, Genetic and Evolutionary Computation Conference, San Francisco, California, USA. • Data provided by colleagues from Institute of Polymer Science and Technology, Superior Council of Scientific Research, Spain, and Biomaterials Center, Havana University, Cuba Evolution Strategy (ES) • ES optimal parameters  known • Function optimization (Rechenberg 94) • Data and program provided by I. Santibáñez-Koref, FG Bionik und Evolutionstechnik , TU Berlin D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 25. Example: Configuring an ES Evolution Strategy (1, )-ES One parent Number of offspring on each generation (the only individuals that undergo selection) • The ES itself optimizes a quadratic function (sphere model) • ES efficiency: progress to the optimum on each generation • Fixed parameters:  = 10000,  = 1e-30, t = 240 • Free parameters:  and c • Theoretical optima: (, c) = (5, 1) and (5, -1) Experimental settings 60 search trials (ABRRHC) 4 neighbors / configuration 4 solutions to exchange among the AC agents proportion of AC agents / parameter to fine-tune = 2 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 26. Obtained solutions Solution qualities for varying control parameters Total of best singular solutions (including all trials or repetitions) = 2 104 Example of an “acceptable” singular solution (according to its quality) (, c) = (5.15965245, 0.87023895) 0,004 5 4 0,003 3 quality quality 0,002 2 0,001 1 0 0 1,00 3,00 5,00 7,00 9,00 -9,97 -7,15 -4,77 -2,42 -0,73 1,70 3,90 6,63 9,21 lambda c D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 27. Pareto-optimal singular solutions 60 50 40 Weight vector quality 30 (w1, w2, w3) = (1.0, 0.0, 0.0) 20 10 0 The importance goes to the solution qualities 0 1000 2000 3000 4000 5000 6000 time (msec) (when calculating the worth) 60 50 Calculated Pareto-optimal solutions, 40 according to all criteria, quality 30 are presented to the user 20 (GUI + data files) 10 0 0 5000 10000 15000 20000 25000 30000 35000 func.eval. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 28. Configuration process Estimated time per agent: AC 32,8282333 min 800 AS 3,25705868 hours 600 % from the total: Configurators 14,382467 400 Solvers 85,617533 200 Total of algorithm’s evaluations = 10 520 0 ABRRHC Solver (i.e. ES evaluations) time (min) 131,3129333 781,6940833 as all AC agents would as all AS agents would sequentially run sequentially run Other experiments already done with ES and GA: - varying the worth equation and the weight vectors - varying the proportion of agents, exchanges, neighbors per solution, etc. - studying IPs (e.g. HBIP, EIP, RequestIP) in detail D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 29. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 30. Ongoing projects • Optimizing risk strategies in random investment environments (GA, 4 free parameters, 2x agents) (E. Navarro, D. Monett, V. Uc Cetina: Machine Learning Approaches for Investment Strategies --- working paper) • Adapting soccer agents’ movement, RoboCup 3D Simulation League (ANN, 50 free parameters, 1x & 2x agents) New to +CARPS: - Connection Clients or CC agents - TCP/IP communication instead data files transfer - related ontologies, classes, and agent behaviors and interactions • Including other configuration algorithms D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 31. Outline • Introduction – Metaheuristics; Configuration process; Why agents? – PhD: Motivation – Goals - Contributions • Agent-Based Configuration of (Metaheuristic) Algorithms – Architecture; agents; interaction protocols; other details • Experimental results • Ongoing projects • Conclusions and Contributions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 32. Conclusions and Contributions +CARPS … is an agent-based approach to the configuration of algorithms including, but not limited to, metaheuristics is a framework that helps monitoring control factors of metaheuristics is a tool useful for conducting experiments when executing these algorithms follows a theoretical description and formalization of the configuration problem D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In Proceedings of the Workshop Concurrency, Specification, and Programming CS&P'2003, volume 2, Czarna, Poland. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 33. Conclusions and Contributions +CARPS … consists of different types of autonomous, cooperative agents that support the configuration of metaheuristic algorithms in a distributed fashion implements a Random-Restart Hill-Climbing algorithm to search for solutions during the configuration process includes new interaction protocols that are followed by the agents in order to cover communication requirements needed in the domain of analysis is a prototype implemented over JADE that follows FIPA specifications provides an infrastructure for a distributed, remote and parallel execution of configuration algorithms (i.e. ontologies, interaction protocols, agent behaviors, and other supporting classes) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 34. Agent-Based Configuration of (Metaheuristic) Algorithms - Doctoral dissertation - M.Sc. Dagmar Monett Díaz Artificial Intelligence, Computer Science Dept. Humboldt University of Berlin 25.02.2005
  • 35. Extra, additional slides - Used in answers, examples, discussion, etc. - M.Sc. Dagmar Monett Díaz Artificial Intelligence, Computer Science Dept. Humboldt University of Berlin 25.02.2005
  • 36. “Fine-tuning” of metaheuristics Auto-adaptive metaheuristics… Brute force - Every configuration is tested and the best one is selected Meta-evolution as “configurator” - Evolutionary algorithms fine-tune other evolutionary algorithms - Examples: meta-level Evo.Alg. GA that produces ES+GA meta-solutions They optimize It optimizes a benchmark single case of functions GA GA the sphere model [Grefenstette, 1986] [Bäck, 1994] [Pham, 1995] [Pedroso, 1997] D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 37. More general “configurators” Using experimental design - Statistical design + gradient descent [Coy et al., 2000] (two local search heuristics) - Fractional experimental design + local search [Adenso-Díaz and Laguna, 2003] (Tabu Search and Simulated Annealing) Racing algorithms - Sequentially evaluate candidate configurations and discard poor ones as soon as statistically sufficient evidence is gathered against them - Example: F-Race: based on a statistical method for hypothesis testing [Birattari et al., 2002] (MAX-MIN-Ant System) Parallel approaches - Independent runs: master-slave approach [Blesa and Xhafa, 2004] (Tabu Search) - Parallel implementations of metaheuristics have also contributed to the topic… D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 38. Auto-adaptive metaheuristics Examples of mechanisms that are used: • Genetic operators encoded as part of the representation of individuals (e.g. ES, first Evo.Alg. that considered self-adaptation) • Variable population size / chromosomal length (e.g. GA) • Mutation schedules for varying the mutation of individuals at early and later evolution stages differently (e.g. GA) E.g.: non-uniform mutation r : random in [0, 1] v  (t ,UBk  vk ), r  0.5 T : max. generation number v 'k   k b : system parameter determining  vk   (t , vk  LBk ), r  0.5 the degree of dependency on (1 t )b iteration number t (e.g. b=5)  (t , y )  y  (1  r T ) LB, UB: upper & lower bounds • Cooling schedules for controlling the search and the probability of accepting worsening solutions (e.g. SA) t : temperature E.g.: geometric and arithmetic cooling r : random in [0, 1] f ( s ) f ( s ') t  t   ,  0 s, s’: solutions e t r ? t  t   ,0    1 f: solution quality D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 39. Constructing neighbor configurations AC agents construct neighbor configurations ISM agent manages the instantiation strategies Example: The new parameter value is randomly generated in a neighborhood of the current value, with NeighborsPercent sizeN  index ( currentNei ghbor ) sizeN : size of the neighborhood NeighborsPercent : percent of the original restriction to the free parameter index(currentNeighbor) : number of neighbors already constructed + 1 • As the number of neighbors increases, it decreases the ratio of the neighborhood around the current parameter value • Decreasing the neighborhood size adds lower noise to the current parameter value • Considering other adaptive techniques  local to the ISM agent !! D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 40. Stop criteria TS 1 TTenure=10 1 TTenure=20 1 TTenure=50 & & & fixed factors fixed factors fixed factors . . . NTrials = ?? . . + . . + . . NTrials TTenure=10 NTrials TTenure=20 NTrials TTenure=50 & & & fixed factors fixed factors fixed factors • Metaheuristics are not exact methods • Goal: To find “acceptable” solutions beyond local optima • Challenge: In less trials as possible • One approach: Stop when the fluctuations of averaged solution qualities with the same configuration is not significant any more (-solution “stability”): i i 1 s s j 1 j j 1 j si  si 1     NTrials = i+1 i i 1 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 41. Stability transition trial Other performance indexes that could be considered: - CPU time to get the solutions - number of function evaluations Related definitions: -computational time “stability” -evaluations “stability” However, it can happen that NTrials for -solution “stability”  NTrials for -computational time “stability”  NTrials for -evaluations “stability” Stability transition trial Τ: Minimal number of trials at which the metaheuristic is solution, computational time and evaluations stable when evaluated with the same configuration D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 42. Expectation value From Experimental Physics: • Given: N independent measurements of a physical constant • Mean of the measurements (“estimate” of the true value): N 1 y N yi 1 i N 1 • Expectation value or true value:   y  lim N  N yi 1 i Expectation value of the solution qualities from the program outputs y(∙) when evaluating the metaheuristic with the same configuration, say p T 1 Correction: E y ( p) s   y( p) s  lim s i T: stability transition trial T  T i 1 However, y(∙) may be not (and similarly for computational time, function normally distributed… evaluations or any other performance index…) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 43. Expectation value Idea: what to expect from repeated outcomes • Discrete random variable X with values x1, x2, …, xN • Probability function f(xi)=P(X= xi) (probability of obtaining each xi) • Expectation value: Special case: If f ( x )  1 N i N E X    xi  f ( xi ) then N 1 i 1 E X   x i X N i 1 Expectation value of the solution qualities from the program outputs when evaluating the metaheuristic with the same configuration (and similarly for computational time, function evaluations or any other performance index…) • Probability function is unknown • It can be estimated after the trials are done, i.e. by simulation • Like this: Relative frequencies could be tabulated. They are estimates of the probabilities 1 Correction: f ( si )  is difficult to have… T D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 44. Goal of the experiments: Twofold Testing the system  How to use +CARPS: conducting experiments & monitoring the configuration process • Aim: to seek for optimum values to the user problem’s parameters • User problem: Chemical optimization problem (Parameter estimation in chemical reactions) • Former obtained solutions are reproduced; better ones are found Evaluating the system  How does +CARPS work: functionality of the system • Aim: to seek for optimum values to the algorithm’s parameters • Configuration algorithm is tested • Also communication among the agents, interaction protocols, etc. • User problem: of secondary importance at this level D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 45. Technical information +CARPS (by August 2004) Agent framework: JADE 3.2 Programming language: JavaTM 2 SDK Standard Ed. 1.4.2 Development tool: BlueJ 2.0 beta Source code lines:  22 500 (most important) Classes 113 (.java) GAs (by 2002) Programming language: Visual C++ 5.0 Development tool: Microsoft Developer Studio 97 Source code lines:  6 000 Total of classes: 10 (.cpp) + 10 headers (.hpp) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 46. Agent-based configuration Optimization Simulation Optimization Agent-based Algorithm Algorithm configuration execution functioning D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 47. +CARPS : Development • software development: it was carried out in an evolutionary, object-oriented fashion • bottom-up strategy: behaviors and agent actions and objects were designed, implemented, debugged, and tested as the needs arose, from simple to more complex components. For example, a draft for the configuration ontology was first considered which later turned into the four vocabularies and ontologies • GUI: it was very important for testing agent interactions and their functioning • JADE (Java Agent DEvelopment Framework) – FIPA specifications • +CARPS classes are Java classes D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 48. Motivation – Goals – Contributions Real problem user problems Motivation: (metaheuristic) algorithms  Powerful algorithms are needed to solve several real problems Developer  Configuring them can be a very difficult combinatorial problem System D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 49. Motivation – Goals – Contributions Real problem user problems Goals: (metaheuristic) algorithms  To support the configuration process of these algorithms Developer . Autonomously, distributed, remote, etc.  To (semi)automate both monitoring and System fine-tuning of parameters and conducting experiments D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 50. Motivation – Goals – Contributions Real problem +CARPS user problems Contributions: (metaheuristic) algorithms  +CARPS, agent-based approach for configuring . Distributed, collaborative problem solving Developer . Necessary information specialization/processing System . Flexibility: important D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 51. Agent-based Configuration: Steps I: Initializationof variables to manage solutions, agent Initialization of variables to manage solutions, agent communications, and conditions for stop criteria. Initialization of the search procedure. II: Constructionof starting configurations with initial levels for for the Construction of starting configurations with initial levels the free parameters. III: Agent-based configuration: application the search procedure Agent-based configuration: application of of the search procedure and exchange of best-so-far solutions among the agents, until stop criteria meet. IV: Organization of partialand global solutions. Report best-so-far partial and global solutions. Report best-so-far configuration and Pareto-optimal solutions. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 52. Why agents for configuring? Distributed execution The (metaheuristic) algorithms and the agents could be physically distributed over a network. Remote execution Local agents can interact with other agents situated on remote computers, thus allowing for the remote execution of algorithms, which do not need to be located where the users are. Cooperation Agents can decide whether to cooperate or not, as well as to ask for Cooperation, if needed, in order to solve the original configuration problem. Furthermore, they can cooperate by exchanging best-so-far obtained solutions. Autonomy & specialization Agents that interact with the users do not need to know how to configure algorithms, nor to solve them or to manage solutions (and vice versa), for example, in order to operate and to have control over their actions. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 53. Why agents … Exploration of the search space Subproblems are considered so as to cover the complete search space as well as possible. Exploitation of the search space It is done by studying a free parameter in detail. The more different parameter variations are considered, the wider the analysis and study of the related parameter. Incremental quality solution Solutions are improved by each agent when applying the configuration algorithm and solutions received from other agents can also improve the ones obtained so far. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 54. Fine-tuning (II) Example by using GA Factor in study: Population size, PopSize Levels: 50, 100, 150 (3 configurations) Number of runs for each configuration: NRuns 1 PopSize=50 1 PopSize=100 1 PopSize=150 + + + fixed factors fixed factors fixed factors . . . . . . . . . NRuns PopSize=50 NRuns PopSize=100 NRuns PopSize=150 + + + fixed factors fixed factors fixed factors  (Factori * Levelj * Runsk ) Which is the “most acceptable” solution? Which is the “best-so-far” configuration? D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 55. Fine-tuning (III) tunable parameters (or factors) = controllable parameters = free parameters parameter tuning = fine-tuning = parameter setting = configuring = configuration process Configuration a specific setting or combination of free and fixed parameters Restrictions define the levels or different values that parameters may have D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 56. Worth of a metaheuristic Pareto-optimum: Situation where it is not possible to improve (to decrease) the value of an objective function without deteriorating (increasing) that of at least one other Example of worth equation (using the weighted sum approach) worth(C )  w1   s2  w2   t2  w3   v2 where: m std. deviations m  x  xi 2 x i 1 i Normalization (e.g.): 2 x  i 1 , x 2 x variances m averages m max(  s2 ,  t2 ,  v2 ) D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 57. Agent-based Config.: Algorithm Configuration ABRRHC(c, p) { // c : initial configuration from SCB agent // p : index of the parameter to fine-tune bestConfiguration = c; i = j = k = 0; do while (i < MaxExchanges and condition1) { do while (j < MaxTrials and condition2) { do while (k < MaxNeighbors and condition3) { nc = neighborConfiguration(c, p); evaluate(nc); if quality(nc) ≤ quality(bestConfiguration) then bestConfiguration = nc; k++; } c = bestNeighbor(); if isRestartAllowed then { rc = restartConfiguration(); evaluate(rc); if quality(rc) ≤ quality(bestConfiguration) then bestConfiguration = rc; c = rc; } j++; } if isExchangeAllowed then { ec = exchangeSolution(bestSolution); if quality(ec) ≤ quality(bestConfiguration) then bestConfiguration = ec; c = ec; } i++; } return bestConfiguration; } // end ABRRHC
  • 58. +CARPS : Development & tools • JADE (Java Agent DEvelopment Framework) • +CARPS classes are Java classes. • +CARPS packages enclose agents, ontologies, and utilities, separately. • Classes & hierarchies will be presented as they are showed when using the BlueJ Java development tool. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 60. Config vocabulary and ontology D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 61. InstStrategy vocabulary and ontology D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 62. +CARPS Agents Types of agents UM User Mediators ISM Instantiation Strategy Managers SCB Starting Configuration Builders AC Algorithm Configurators AS Algorithm Solvers SM Solution Managers - Agent communication: relevant - Specialization and distributed information processing: relevant - Following the standards (e.g. FIPA specifications): important D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 63. SCB agent : Example P  D1 , P2 , P3 1 s1b  randInit ,   b   s2  lowLevelInit ,  SCB1 ST1   b  s3  uppLevelInit , s b  avgInit   4  level11 , level12 , level13 , level14 C1 : level11 , P2 , P3  C3 : level13 , P2 , P3  C 2 : level12 , P2 , P3  C 4 : level14 , P2 , P3  D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 64. Algorithm Configurators: IPs AC & AS agents help UM agents with the configuration process UM agents book their services • HBIP : Helper-Booker Interaction Protocol – HB Initiator Finite State Machine (AC & AS) – HP Responder Finite State Machine (UM) • HelperBookerProtoASResponder.java • HelperBookerProtoACResponder.java D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 65. Helper-Booker-Protocol AS, AC UM Initiator Participant propose reject-proposal failure accept-proposal inform failure [dead- line1] confirm cancel [deadline2] D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 66. Helper-Booker-Initiator-FSM SEARCH_DF DELAY 1 VERIFY_REGISTERED_AGENTS REMOVE_BOOKER 2 4 12 SND_PROPOSAL 7 SND_CANCEL 3 10 6 RCV_RESPONSE RCV_DATA 5 8 9 SND_RES_NOTIFICATION 11 TERMINATE_PROTOCOL MAKE_CLONE
  • 67. Helper-Booker-Responder-FSM RCV_PROPOSAL 1 2 VERIFY_BOOKED_AGENTS 4 3 13 SND_REJECT_PROPOSAL SND_ACCEPT_PROPOSAL 5 8 6 SND_DATA 7 RCV_RESPONSE 10 9 11 12 INCREASE_BOOKERS SND_CANCEL VERIFY_TERMINATION 14 TERMINATE_PROTOCOL
  • 68. Agents’ communications Most relevant IPs in +CARPS Engagement-Protocol AC AS Interaction Protocols Initiator Participant query-if Helper-Booker IP refuse Engagement IP inform [deadline1] Request IP confirm [deadline2] cancel - Which agents will solve the algorithms? - Among AC agents (configurators) and AS agents (solvers) - Particular case of the standard FIPA Query IP - Configurators are initiators of the EIPs - Solvers are responders D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 69. Algorithm Configurators: IPs An engagement is a compromise, a contract, between two parts AS agents solve the algorithm being configured Engagement Interaction Protocol AC agents need AS agents in the configuration process D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 70. Algorithm Configurators: IPs • HBIP : Helper-Booker Interaction Protocol – HB Initiator Finite State Machine (AC & AS) • HelperBookerProtoInitiator.java – HP Responder Finite State Machine (UM) • EIP : Engagement Interaction Protocol – E Initiator Finite State Machine (AC) • EngagementProtoInitiator.java – E Responder Finite State Machine (AS) • Request Interaction Protocol – Request Initiator Finite State Machine (AC) • RequestProtoInitiator.java D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 71. Algorithm Configurators: IPs Engagement-Protocol AC AS Initiator Participant query-if refuse inform [deadline1] confirm cancel [deadline2] D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 72. Algorithm Configurators: IPs Engagement-Initiator-FSM SELECT_SOLVER 2 SND_QUERY_IF REMOVE_SOLVER 1 4 6 5 RCV_RESPONSE SND_RES_NOTIFICATION 3 7 TERMINATE_PROTOCOL D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 73. Engagement-Responder-FSM 1 RCV_QUERY_IF 2 VERIFY_CONDITIONS 10 4 3 SND_REFUSE SND_INFORM 5 6 RCV_RESPONSE 7 8 9 ENGAGE SND_CANCEL VERIFY_TERMINATION 11 TERMINATE_PROTOCOL D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 74. Agents’ communications Most relevant IPs in +CARPS FIPA-Request-Protocol Initiator Participant Interaction Protocols request refuse Helper-Booker IP [refused] agree [agreed and Engagement IP notification necessary] Request IP failure [agreed] inform-result: inform - Solving the algorithms - Among AC agents (configurators) and AS agents (solvers) - FIPA Request IP - like - Configurators are initiators of the EIPs - Solvers are responders D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 75. Agents’ communications Request-Initiator-FSM SND_REQUEST 8 1 RCV_RESPONSE 3 2 5 RCV_RES_NOTIFICATION UPDATE_CONTENT 4 6 7 9 RCV_DATA TERMINATE_PROTOCOL D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 76. Solver-FSM PREPARE_DATA 1 VERIFY_REPETITIONS EXEC_ALGORITHM 5 4 3 2 VERIFY_SOLUTIONS UPDATE_COUNTER 6 7 PROCESS_RESULTS CONSTRUCT_SOLUTIONS UPDATE_VARIABLES PROCESS_ERR_CODE TERMINATE_SOLVER D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 77. AC-AS communications paths to M and io files, par. names, ag. lists data configs AC AS M solutions results input files config AC AS M .exe singular solution output files D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 78. +CARPS : Tree +carps src agent AC behaviours AS ISM Agents and their behaviors SCB SM UM metah copga Algorithms to configure evoes ontology agentList configuration Vocabularies and ontologies strategy userproblem util comm graphics gui Utilities io jExtra table D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 79. <time.h> GA <windows.h> time.hpp time.cpp commonDef.hpp <math.h> random.hpp random.cpp <assert.h> <fstream.h> utility.hpp utility.cpp <iostream.h> gene.hpp gene.gpp <iomanip.h> chromosome.hpp chromosome.cpp objfunc.hpp objfunc.cpp population.hpp population.cpp ga.hpp ga.cpp <ctype.h> <stdlib.h> copga.hpp copga.cpp <string.h> mainGA.cpp D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 80. Agents and containers AC1 AC2 DF AMS RMA UM1 SM1 ISM1 SCB1 Main container AS1 AS2 Container 1 Container 2 Platform 1 Network UM2 ISM2 SM2 SCB2 AS3 AS4 DF AMS RMA DF AMS RMA Main container Main container Platform 3 Platform 2
  • 84. ES: c vs. quality theoretical value for the optimum: c = 1 and c = -1 60 5 50 4 40 3 quality quality 30 2 20 1 10 0 0 -9,97 -7,15 -4,77 -2,42 -0,73 1,70 3,90 6,63 9,21 -9,97 -7,15 -4,77 -2,42 -0,73 1,70 3,90 6,63 9,21 c c 1 0,004 0,8 0,003 quality 0,6 quality 0,002 0,4 0,001 0,2 0 0 -10,0 -8,00 -6,00 -4,00 -2,00 0,00 2,00 4,00 6,00 8,00 10,00 -9,97 -7,15 -4,77 -2,42 -0,73 1,70 3,90 6,63 9,21 0 c c D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 85. 1prop&4n 10 8 6 4 2 0 c -2 -4 9,0E+03 -6 8,0E+03 -8 7,0E+03 -10 time (msec) 6,0E+03 0 2 4 6 8 10 5,0E+03 lambda 4,0E+03 3,0E+03 9,0E+04 2,0E+03 8,0E+04 1,0E+03 7,0E+04 0,0E+00 6,0E+04 0 5 10 15 20 25 30 35 40 45 func. eval. 5,0E+04 quality 4,0E+04 3,0E+04 2,0E+04 1,0E+04 0,0E+00 0 5 10 15 20 25 30 35 40 45 quality D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 86. Testing engagements Elapsed time of the Engagement IPs - Proportion of AC agents per parameter to fine-tune = 2 - Intra-platform communication - AC agents run in a main container; AS agents, in a secondary one - Number of parameter to fine-tune = 2 ( and c) 25 20 4 AC agents & 4 AS agents time (sec) 15 10 Engagements: 5 AC1-AS2 (or AC1-AS4) AC2-AS3 0 1 2 3 4 AC3-AS1 AC 0,094 11,688 23,359 0,094 AC4-AS4 (or AC1-AS2) AS 23,875 0,359 12,11 0,344 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 87. Pareto-optimal singular solutions 60 50 40 Weight vector (w1, w2, w3) = (1.0, 0.0, 0.0) quality 30 20 10 0 The importance goes to the solution qualities 0 1000 2000 3000 4000 5000 6000 (when calculating the worth) time (msec) 60 50 40 Four best Pareto-optimal singular solutions according to their qualities quality 30 20 10 lambda c quality 0 5,15965245 0,87023895 3,98E-05 0 5000 10000 15000 20000 25000 30000 35000 4,86909981 -0,98344576 5,70E-05 func.eval. 4,4049138 -1,00838506 3,43E-04 4,83154784 -1,04477195 3,57E-04 Total of Pareto-optimal = 388 singular solutions D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 88. (cont.) 1,2E+04 1,0E+04 time (msec) 8,0E+03 6,0E+03 4,0E+03 2,0E+03 4,0E+04 3,5E+04 0,0E+00 1 2 3,0E+04 time (msec) AC 10985 94 2,5E+04 AS 11266 141 2,0E+04 1,5E+04 1,0E+04 6,0E+04 5,0E+03 0,0E+00 5,0E+04 1 2 3 4 109 35843 11000 11015 time (msec) 4,0E+04 AC AS 11375 203 11422 36172 3,0E+04 2,0E+04 1,0E+04 0,0E+00 1 2 3 4 5 6 AC 156 57953 13500 125 93 156 AS 437 14687 469 438 469 59328 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 89. (cont.) 14 12 10 best quality 8 6 4 2 0 2,50E-03 4,00E-03 1 30 59 88 117 146 175 204 233 262 3,50E-03 2,00E-03 t 3,00E-03 1,50E-03 best worth 2,50E-03 8n 6n 4n 2n 1,00E-03 2,00E-03 1,50E-03 5,00E-04 1,00E-03 0,00E+00 5,00E-04 5,0E-04 -5,00E-04 0,00E+00 4,0E-04 1 30 59 88 117 146 175 204 233 262 best quality 3,0E-04 t 2n 4n 8n 6n 2,0E-04 1,0E-04 0,0E+00 1 30 59 88 117 146 175 204 233 262 t 4n 8n D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 90. q9vp 1,8 1,6 r2 1,4 1,2 15 1 12 best fitness 2 2,5 3 3,5 4 9 r1 6 3 0,09 0 0,088 1 10 19 28 37 46 55 64 73 82 91 100 0,086 population best distance 0,084 0,082 current best-so-far 0,08 0,078 0,076 0,074 1 10 19 28 37 46 55 64 73 82 91 100 population current best-so-far D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 91. 2n&repeat 1,4E+06 1,2E+06 1,0E+06 time (msec) 8,0E+05 6,0E+05 4,0E+05 2,0E+05 0,0E+00 2n-1 2n-2 2n-3 2n-4 2n-5 mean Solver 911399 798396 1136179 982935 992848 964351,4 ABRRHC 99601 87823 108009 112955 103573 102392,2 1,4E+06 1,2E+06 1,0E+06 time (msec) 8,0E+05 6,0E+05 4,0E+05 2,0E+05 0,0E+00 2n-1 2n-2 2n-3 2n-4 2n-5 Solver 911399 798396 1136179 982935 992848 ABRRHC 99601 87823 108009 112955 103573 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 92. (cont.) 2,5E+07 2,0E+07 time (msec) 1,5E+07 1,0E+07 5,0E+06 0,0E+00 1x 2x 3x Solver 2414233 7422779 16841628 ABRRHC 185971 1141846 3319888 60 50 40 time (min) 30 20 10 0 1x 2x 3x Solver 20,11860833 30,92824583 46,7823 ABRRHC 1,549758333 4,757691667 9,221911111 D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 93. Related publications D. Monett (2004). Interaction Protocols for +CARPS Agents: Booking and Getting Engaged for Configuring. In G. Lindemann, H.-D. Burkhard, L. Czaja, A. Skowron, H. Schlingloff, Z. Suraj, editors, Proceedings of the Workshop Concurrency, Specification, and Programming CS&P'2004, volume 3: Multiagent Systems and Applications, pages 507-518, Caputh, Germany. Informatik-Bericht Nr. 170. (Also in Special Issue of Fundamenta Informaticae –to appear–) D. Monett (2004). Collaborative JADE Agents Enabling the Configuration of Algorithms. In D. Khadraoui, editor, Proceedings of the International Conference on Advances in Intelligent Systems - Theory and Applications, AISTA'2004, IEEE Computer Society, University of Canberra and CRP Henri Tudor, Luxembourg-Kirchberg, Luxembourg. D. Monett (2004). +CARPS: Configuration of Metaheuristics Based on Cooperative Agents. In Ch. Blum, A. Roli, M. Sampels, editors, Proceedings of the First International Workshop on Hybrid Metaheuristics, HM'2004, at the 16th European Conference on Artificial Intelligence, ECAI'2004, pages 115-125, Valencia, Spain. D. Monett, J.A. Méndez, G.A. Abraham, A. Gallardo, J. San Román (2002). An Evolutionary Approach to Reactivity Ratios Prediction. Macromol. Theory Simul., 11(5):525-532. Wiley-VCH Verlag GmbH, Weinheim, Germany. D. Monett (2001). On the automation of evolutionary techniques and their application to inverse problems from Chemical Kinetics. In C. Ryan, editor, Proceedings of the GECCO'01 Graduate Student Workshop, Genetic and Evolutionary Computation Conference, pages 429-432, San Francisco, California, USA. D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In L. Czaja, editor, Proceedings of the Workshop Concurrency, Specification, and Programming CS&P'2003, volume 2, pages 353-364, Czarna, Poland. Zakład Graficzny UW, zam. 591/2003. D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 94. Conclusions and Contributions +CARPS, agent-based approach to the configuration of algorithms including, but not limited to, metaheuristics is proposed +CARPS consists of different types of cooperative agents that support the autonomous configuration of metaheuristic algorithms and that follows FIPA specifications in a distributed fashion Ontologies, interaction protocols, agent behaviors, and other supporting classes conform the +CARPS infrastructure for the agent-based configuration Conception and development of new interaction protocols that are followed by the agents in order to cover communication requirements +CARPS needed in the domain of analysis I/O Layer Algorithm Configuration Layer Algorithm Solution Layer Communication Layer D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005
  • 95. Conclusions and Contributions Theoretical description and formalization of the configuration problem D. Monett (2003). Configuration of Metaheuristics: Overview and Theoretical Approach. In Proceedings of the Workshop Concurrency, Specification, and Programming CS&P'2003, volume 2, Czarna, Poland. Implementation of a Random-Restart Hill-Climbing algorithm that some specialized agents apply to search for solutions during the configuration process +CARPS is also a framework in which monitoring of control factors of metaheuristics can be easily made At the same time, it can be seen as a powerful tool useful for conducting experiments when executing metaheuristic algorithms +CARPS I/O Layer Algorithm Configuration Layer Algorithm Solution Layer Communication Layer D. Monett Díaz Humboldt University of Berlin Doctoral dissertation, 25.02.2005