A presentation conducted by Mr Mehrdad Amirghasemi, SMART Infrastructure Facility, University of Wollongong. Presented on Wednesday the 2nd of October 2013. Modelling and simulation for improved infrastructure is involved with the development of adaptive systems that can learn and respond to the environment intelligently. Developing simple agents with limited intelligence that collectively represent complex behaviour can assist infrastructure planning and can model many real world situations. By employing sophisticated techniques which highly support infrastructure planning and design, evolutionary computation can play a key role in the development of such systems. The key to presenting solution strategies for these systems is fitness landscape which makes some problems hard and some problems easy to tackle. Moreover, constructive methods and local searches can assist evolutionary searches to improve their performance. In this paper, all these four concepts are reviewed and their application in infrastructure planning and design is discussed. With respect to applications, the main emphasis includes city planning, and traffic equilibrium.

1. ENDORSING PARTNERS
The roles of evolutionary
computation, fitness landscape,
constructive methods and local searches
in the development of adaptive systems
Monday, 30 September 2013: Business & policy Dialogue
for infrastructure planning
The following are confirmed contributors to the business and policy dialogue in Sydney:
•
Rick Sawers (National Australia Bank)
•
Nick Greiner (Chairman (Infrastructure NSW)
th
Tuesday 1 October to Thursday,
Dialogue
3rd
October: Academic and Policy
www.isngi.org
Presented by: Mr Mehrdad Amirghasemi, SMART Infrastructure Facility, University
of Wollongong
www.isngi.org

2. The Roles of Evolutionary Computation,
Fitness Landscape, Constructive Methods
and Local Searches in the Development of
Adaptive Systems for Infrastructure Planning
Mehrdad Amirghasemi
Reza Zamani

3. The roles of evolutionary computation, fitness landscape,
constructive methods and local searches in the development
of adaptive systems for infrastructure planning

4. Outline
• Introduction
– Infrastructure problems.
• Simple representative problems.
• Hybrid model description.
• Benchmark test results.

5. Infrastructure
• Connected elements which are structurally related and
each element affects other elements.
• Involved with the optimized selection of a set of values,
among a set of alternatives, for a number of variables.
• Interaction among variables makes these problems
extremely hard.
• Sophisticated optimization techniques and mathematical
programming are the key issues in reaching a reasonable
level of efficiency in solving infrastructure problems.

6. Z
Infrastructure problems difficulty
(0,0,0)
X
Number of Variables
Probabilistic Nature
Ti
m
eH
or
iz
on
(1,1,1)
Y

7. Simplified problem: minimizing traffic
Library(1)
A
Avg. traffic
Hospital (2)
distance
University (5)
C
B
E
D
A
B
C
D
E
A
B
C
D
E
1
4
2 3
π1
5
5
1
3 2
π2
4
Shopping Centre (4)
Sport Centre (3)

8. The complexity of the problems
𝑛
𝑛
Traffic_Volume(𝜋) = ∑ 𝑖=1 ∑ 𝑗=1 𝑓𝑖𝑖 𝑑 𝜋 𝑖 𝜋(𝑗)
The problem is called Quadratic Assignment Problem(QAP).
As the problem size increases linearly, the size of solution space
increases exponentially.
5!=5×4×3×2×1=120
60! = 60×59× … ×2×1 = 8×1081
With an 8 Ghz processor, evaluating all possibilities takes 1066
years of computation time. The age of the universe is around
1010 years.
• Simplified mathematical model:
•
•
•
•
•

9. Speed of computers
• This “intractability” is despite the fact that the computer
industry has progressed so fast.
“If the car industry moved as fast as the computer
industry, cars would get 470, 000 mph, 100,000 miles
per gallon, and would cost three cents.”
–Paul Otellini, Intel CEO.
• This necessitates a need for better algorithms that are
capable of yielding high-quality solutions in a reasonable
amount of time.

10. The proposed hybrid meta-heuristic
• The aim is to develop effective
problem-solving procedures to obtain
high quality solutions fast.
• Generally the designed algorithms
consist of four modules:
– A constructive procedure to produce
a pool of high quality initial
solutions.
– A Local Search procedure to
improve a given, complete solution.
– A population based (evolutionary)
module to combine solutions from
the current pool.
– A Synchronizer module, which
facilitate interaction among the
above three modules.
Constructive
Method
Synchronizer
Local Search
Genetic
Algotihm

11. Fitness landscape
• When solved with local search methods, the difficulty of an
optimization problem is directly related to the shape of its
fitness landscape.
The fitness landscape of a hard optimization problem
The fitness landscape of an easy optimization
problem

12. Modular design
• Based on “No-free-lunch” theorem
(Wolpert and Macready 1997),
incorporating problem-specific
knowledge, and matching the
“procedure” with the “problem” is
essential for developing high
performance procedures.
• The ideal case is to have a generic
procedure to handle similar problems.
• A modular design helps to achieve a
balance between the above two facts.
12

13. Benchmark test results
• Designed Algorithms have been applied to three wellknown problems in the literature:
– Quadratic Assignment, QAP
– Job Shop Problem, JSP
– Permutation Flow Shop Problem, PFSP
• Competitive results have been achieved on all benchmark
tests.
• For JSP, a notorious instance is solved 6 times faster the
fastest available method in the literature.

14. References
Boese, K. D., A. B. Kahng, et al. (1994). "A new adaptive multi-start technique for
combinatorial global optimizations." Operations Research Letters 16(2): 101-113.
De Jong, K. A. (2006). Evolutionary computation: a unified approach, MIT Press.
Fogel, D. B. (1994). "An introduction to simulated evolutionary optimization." Neural
Networks, IEEE Transactions on 5(1): 3-14.
Jones, T. and S. Forrest (1995). Fitness Distance Correlation as a Measure of Problem
Difficulty for Genetic Algorithms. Proceedings of the 6th International Conference on
Genetic Algorithms, Morgan Kaufmann Publishers Inc.: 184-192.
Wolpert, D. H. and W. G. Macready (1997). "No free lunch theorems for optimization."
Evolutionary Computation, IEEE Transactions on 1(1): 67-82.
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15. Mehrdad Amirghasemi
PhD student
ma604 (at) uow.edu.au
15