1. From:
http://www.it.uom.gr/pdp/DigitalLib/EC/ec_soft.htm
Shareware - Freeware
• BUGS (Better to Use Genetic Systems) is an interactive program for
demonstrating the Genetic Algorithm and is written in the spirit of Richard
Dawkins' celebrated Blind Watchmaker software. By Joshua Smith.
• DAGA3.2 is an experimental release of a 2-level genetic algorithm
compatible with the GALOPPS GA software. It is a meta-GA which
dynamically evolves a population of GAs to solve a problem presented to
the lower-level GAs. Developed at GARAGe (Genetic Algorithms
Research and Applications Group), Department of Computer Science,
Michigan State University.
• DaVinci is a X-Window visualization tool for drawing directed graphs
automatically in high quality. DaVinci is developed by Michael Fröhlich
and Mattias Werner from the Group of Prof. Dr. Bernd Krieg-Brückner at
University of Bremen, Germany.
• DGP. Phyllis Chong's DGP is a Java based GP system which allows many
PCs and Workstations to collaboratively evolve programs using either
Java applications or applets across the Internet. Central Java servelets
allow you to control and monitor the distributed population. Java source
code, html and compiled byte code are available via anonymous ftp from
Birmingham Univ. and mad-scientist. A demonstration page is currently
online (but will be removed shortly).
• DGenesis is a distributed implementation of a Parallel Genetic
Algorithm. It is based on John Grefenstette's GENESIS 5.0.
Each subpopulation is handled by a Unix process and
communication between them is handled with Berkeley sockets. The user
can set the migration rate, the migration interval and the topology of
communication between subpopulations. By Erick Cantu-Paz
• EvolC, is a general evolutionary software. Written in C++, it includes the
following features: Easy parametrization (through parameter file or
command-line arguments), large choice of selection/replacement
procedures (including standard GAs, ESs, EP and SSGA popular schemes)
through the parameters; you can even build your own without
recompiling, many standard operators on binary and real representations,
including ES-self-adaptive mutations, on-line graphical monitoring of

2. population statistics and restart facilities. By Artificial Evolution and
Machine Learning Group (in french, EEAAX) at Applied Maths Center
(CMAP), Ecole Polytechnique.
• Evolutionary Strategies Toolbox for Scilab. A GNU toolbox for Scilab,
i.e. a free matlab-like software. The toolbox has been used as an heuristic
method for construction of controllers that simultaneously stabilize a finite
collection of SISO (Simple Input Simple Output) plants. Another test was
done to design locomotion structures for a real biped robot. There is only
one fitness function included in this release, for testing purposes
(hypersphere function).Developed by Rodolfo Sánchez-Guzmáan, LINDA
Group, Facultad de Ingenieria de la Universidad Nacional Autonoma de
Mexico.
• FORTRAN Genetic Algorithm (GA) Driver, a Genetic Algorithm
implementation written in Fortran. By David L. Carroll, Aeronautical and
Astronautical Engineering Department, Univ. of Illinois at Urbana-
Champaign.
• Friar Tuck 1.0: A Constraint-based Round Robin Planner. Friar Tuck is a
generic round robin tournament planner that allows to conveniently enter a
variety of constraints. It allows the coordinator of sport tournaments to
compute optimal solutions to complex tournament planning problems. It is
based on constraint programming and implemented in the concurrent
constraint language Oz, using the programming system DFKI Oz 2.0. By
Martin Henz, School of Computing, National University of Singapore.
• GAC, GAL. Simple GAs conceptually based on Genesis. By Bill Spears at
Navy Center for Applied Research in Artificial Intelligence.
• GAGS (Genetic Algorithms from Granada, Spain), is a Genetic Algorithm
application generator and class library written mainly in C++. Made by J.
J. Merelo, Geneura Team, Electronica and Technologia of Computers
Department, University of Granada, Spain.
• GAlib. It is a C++ library that provides the application
programmer with a set of genetic algorithm objects. The
library contains list, tree, array, and binary string
chromosomes with many initialization, crossover, and mutation operators.
It also includes an assortment of selection, scaling, and termination
functions as well as support for overlapping and non-overlapping
populations. GAlib can be used with PVM (parallel virtual machine) to
evolve populations and/or individuals in parallel on multiple CPUs. By
Matthew Wall, Massachusetts Institute of Technology (MIT).
• GALOPPS (the "Genetic ALgorithm Optimized for Portability and
Parallelism System") is a generic 'C' genetic algorithm tool that provides

3. an enormous range of options for genetic algorithm experiments.
Developed at GARAGe (Genetic Algorithms Research and Applications
Group), Department of Computer Science, Michigan State University.
• GAOT, GA Optimization Toolbox (for Matlab) implements
simulated evolution in the Matlab environment using both
binary and real representations. (Ordered base representation
is in the debugging stage.) This implementation is very flexible in the
genetic operators, selection functions, termination functions as well as the
evaluation functions that can be used. Papers on toolbox available. Made
by Meta-Heuristic Research and Applications Group, Department of
Industrial Engineering, North Carolina State University, USA.
• GARP(Genetic Algorithm for Rule-set Production) uses a genetic
algorithm to automate the use of environmental data collected through
field surveys to produce distribution maps and models. This method has
been largely applied to predicting the distribution of species of animals
and plants but can potentially predict any observable environmental entity.
By Environment Australia, the Environment Program of the Australian
Environment Portfolio.
• GAS is a steady state genetic algorithm with subpopulation support. It is
capable of optimizing functions with a high number of local optima. The
parameter setting is based on theoretical results. A paper describing GAS
is also available. By Jozsef Attila University, Szeged, Hungary.
• GP Kernel is a very easy-to-use C++ - class-library for genetic
programming. It was developed by Vienna University of Economics.
• Gaucsd, C/C++ source code. Genesis based GA package incorporating
numerous bug fixes and user interface improvements. By Nicol N.
Schraudolph.
• GECO (Genetic Evolution through Combination of Objects). A toolbox
for prototyping genetic algorithms in LISP. It provides a set of extensible
classes and methods designed for generality. Extensive documentation and
some simple examples are also provided to illustrate the intended use. By
George P.W. Williams Jr.
• Genesis, an updated version of the original first widely available GA
program by John Grefenstette, Navy Center for Applied Research in
Artificial Intelligence.
• GENEsYs. Implementation based on Grefenstette's software package
GENESIS. It includes extensions for experimental purposes, e.g. different
selection mechanisms (linear ranking, Boltzmann selection, (mu, lambda)-
selection) and extended recombination operators (m-point, uniform,

4. discrete and intermediate recombination). Extensive documentation. By
Thomas Baeck, 1992.
• Genetic-2, and Genetic-2N. Both programs aim at solving the linear
transportation problem (minimization of the transportation cost). By
Zbigniew Michalewicz, Dept. of Computer Science, University of North
Carolina at Charlotte.
• GENOCOP. Original version of GEnetic algorithm for Numerical
Optimization for COnstrained Problems. This system is optimizing any
function with any number of linear constraints (equalities and
inequalities). New versions and Genocop III also available. By Zbigniew
Michalewicz, Dept. of Computer Science, University of North Carolina at
Charlotte.
• GPC++ - Genetic Programming C++ Class Library. The GP kernel is a C+
+ class library that can be used to apply genetic programming techniques
to all kinds of problems. The library defines a class hierarchy. An integral
component is the ability to produce automatically defined functions as
found in Koza's Genetic Programming II. Technical documentation in
postscript format is available. There is also a short introduction into
genetic programming. By Thomas Weinbrenner, Institute for
electromechanical constructions, Darmstadt University of Technology,
Germany.
• JDEAL -The Java Distributed Evolutionary Algorithms Library. JDEAL is
an object-oriented library of Evolutionary Algorithms, with both local and
distributed algorithms, for the Java language. JDEAL features include:
high quality implementations of evolutionary algorithms (genetic
algorithms, evolutionary strategies, ...); easy integration of specific
operators, chromosomes and algorithms; reuse and extension of existing
components for faster development times; clean design and architecture;
extensive documentation; distributed and parallel implementations of the
algorithms; source code available; free for non-commercial and non-for-
profit activities. Developed at LaSEEB - Evolutionary Systems and
Biomedical Engineering Lab., Instituto de Sistemas e Robótica, Instituto
Superior Técnico, Lisbon, Portugal
• JavaSANE software package for evolving neural networks with genetic
algorithms is available from the UTCS Neural Networks Research Group
website. The SANE method has been designed as part of our ongoing
research in efficient neuro-evolution. This software is intended to facilitate
applying neuro-evolution to new domains and problems, and also as a
starting point for future research in neuro-evolution algorithms.

5. • Koza.gp is a pure (CLtL2) Common Lisp implementation of the Genetic
Programming Paradigm, as described in Genetic Programming by John R
Koza, MIT Press, 1992.
• libga100, GA library written in C. Simple, easy to use, many knobs to
turn. Both generational and steady state models supported. Many standard
operators. Config file obviates recompilation. Function pointers to all
operators.
• lil-gp is a generic 'C' genetic programming tool. It was written with a
number of goals in mind such as speed and ease of use and also supports a
wide range of options. Developed at GARAGe (Genetic Algorithms
Research and Applications Group), Department of Computer Science and
Engineering, Michigan State University
• EPG is a spanish application (interface translated in English) based on lil-
gp. It runs under Windows 95/98. It is easy to upgrade lil-gp user
problems to EPG because it maintains about a 95% of lil-gp capabilities,
as well as it includes new powerful features:
o Graphical interface for input parameters.
o Start, pause, continue the actual run.
o Increase the max_generations limit when reached if you want to.
o Graphical representation of adjusted fitness, structural complexity
and tree depth.
o Exploration of the individuals in the population: hits, depth, nodes,
composition.
o Static or animated evaluation of each individual. Do you want to
see how the artificial ant moves through the trail?. Very little
Win95 graphical knowledge required.
o Sort the population depending on fitness, depth or nodes.
o Easy upgrade from lil-gp files.
o Independent kernel. User problems implemented in DLLs.
o And much more!
Developed by Andres del Campo Novales, Universidad de Cordoba.
• Neural Network Using Genetic Algorithms uses GA to find the solution to
a classification problem with a neural network (NN). The neural network
is a structure which is able to respond with True (1) or False (0) to a given
input vector. We are trying to "teach" our neural network to correctly
classify a set of input vectors, which can be thought of as learning a
concept. We then expect that when the neural network will be presented
with a vector P not from this set, it will tend to exhibit generalization by
responding with an output similar to target vectors for input vectors close
to the previously unseen input vector P. By Mathematics and Computer
Science, Ben Gurion University, Israel.

6. • PARAGenesis, a parallel implementation of Grefenstette's genesis
program for the CM-200. By Michael van Lent.
• PGAPack Parallel Genetic Algorithm Library is a general-purpose, data-
structure-neutral, parallel genetic algorithm library. It is intended to
provide most capabilities desired in a genetic algorithm library, in an
integrated, seamless, and portable manner. By David Levine, Mathematics
and Computer Science Division, Argonne National Laboratory, USA.
• PGA, Parallel Genetic Algorithms testbed. PGA is a simple testbed for
basic explorations in genetic algorithms. Command line arguments control
a range of parameters, there are a number of built-in problems for the GA
to solve. PGA allows multiple populations, with periodic migration
between them, and a range of other options. By Peter Ross, Dept. of
Artificial Intelligence, Univ. of Edinburgh
• REGAL is a distributed genetic algorithm-based system, designed for
learning First Order Logic concept descriptions from examples. REGAL is
based on a selection operator, called Universal Suffrage operator, provably
allowing the population to asymptotically converge, on average, to an
equilibrium state, in which several species coexist. This version of
REGAL is provided with a graphical user interface. A project by
Department of Computer Science at the University of Torino, Italy.
• SGA-C, a C-language translation and extension of Goldberg's SGA
(1991), and SGA-Cube, with modifications for the nCube hypercube
computer. Both by Robert Smith, Dept of Aerospace Engineering and
Mechanics, Univ. of Alabama.
• SGPC is a simple Koza and Rice workalike written in C.
• SUGAL - SUnderland Genetic ALgorithm, subsumes most of the GA
models of which I'm aware as sub-sets of its functionality, and can be
extended to model those it doesn't subsume. Certainly the
Holland/Goldberg/Dejong models, Whitley's Genitor, and Fogel's real
parameter model are covered (and extended to arbitrary datatypes), along
with other more obscure versions. Sugal breaks the features of the various
algorithms into separate parts, so that an extremely extensive range of
hybrids of the standard models is also available. Aspects of evolution
strategy aren't covered (e.g. the mutation rates which are themselves
mutated). A Trajan Software Ltd. project.
Commercial

7. • Evolutionary Optimizer (EVO) is a tool for optimizing any systems whose
properties are determined by numerical parameters (fuzzy controllers, for
example). The approach for optimizing the parameters is adapted from the
biological evolution: A population of several parameter sets represents a
parents generation, which generates children (new parameter sets). By
TransferTech GmbH.
• Explore the FlexTools range of product suites and services. Build
Computationally Intelligent Systems using Soft Computing techniques and
apply them to your diverse application domains. Developed by Flexible
Intelligence Group, LLC.
o FlexTool(ENM): Evolutionary Neuro Modeling Tool
o FlexTool(EFM): Evolutionary Fuzzy Modeling Tool
o FlexTool(GA): Genetic Algorithm Tool: The Optimizer
• Genetic Algorithm Toolbox for Matlab is a collection of specialized
MATLAB functions supporting the development and implementation of
genetic and evolutionary algorithms. It was developed by Andrew
Chipperfield, Carlos Fonseca, Peter Fleming and Hartmut Pohlheim,
Evolutionary Computation in Control Systems Engineering, Department
of Automatic Control & Systems Engineering Department, University of
Sheffield,UK
• Evolver uses genetic algorithm technology to find optimal solutions to
virtually any problem that can modeled in an Excel worksheet. The best-
selling genetic algorithm now works in Windows 95, and comes with lots
of examples, free support, and a developer kit so programmers can add
Evolver's engines to their own custom applications and distribute them
royalty-free. An Axcelis product.
• Model 1 is the first software tool to automatically use a variety of different
modeling techniques (RFM, linear and logistic regression, neural nets,
CHAID, genetic search) to solve your database marketing problems and
tell you which one is best! With an easy-to-use point-and-click interface
and wizards, marketing analysts and modelers alike can successfully
develop and deploy response models, cross-sell models, customer
valuation models, and segmentation and profiling models. A powerful
appplication programming interface (Model 1 API) is available to
customize the data mining engine for your needs. Developed by Unica
Technologies,Inc.
• NeuroForecaster/GENETICA is an advanced windows-based, user-
friendly business forecasting tool. It is packed with the latest technologies
including neural network, genetic algorithm, fuzzy computing and non-
linear dynamics. For time-series analysis, cross-sectional classification
and indicator analysis. By NewWave Intelligent Business Systems, NIBS
Inc.

8. • ActiveGA. An ActiveX control that uses genetic algorithm to find a
solution for a given problem. Now you can utilize this powerful
optimization technique easier than ever before. By Brightwater.
• Partek. Data analysis and modeling package. Includes neural net, fuzzy,
genetic, visualization, variable selection, pattern recognition, and other
tools.
• STATISTICA: Neural Networks is a comprehensive application capable
of designing a wide range of neural network architectures, employing both
widely-used and highly-specialized training algorithms. It offers a number
of unique features such as sophisticated, state-of-the-art training
algorithms, an Automatic Network Designer, a Neuro-Genetic Input
Selection facility, complete API (Application Programming Interface)
support, and the ability to interface with STATISTICA data files and
graphs. Developed by StatSoft, Inc.
• Generator is a special genetic algorithm program which can help you solve
a wide variety of problems, optimization, curve fitting, evolution and
recombination, scheduling, multi-variable problems, optical lens design,
photomask design, stock market projections, electronic circuit design,
neural network design and optimization, non-linear mathematics and
physics problems, business productivity and management theories. By
New Light Industries, Ltd.
• SPLICER. A Genetic Algorithm Tool for Search and Optimization . It can
be used to solve search and optimization problems. Genetic algorithms are
adaptive search procedures based loosely on the processes of natural
selection and Darwinian "survival of the fittest." Splicer provides the
underlying framework and structure for a building a genetic algorithm
application. By Cosmic, NASA's Partner for Software Technology
Transfer.
• VisualMath is a user-friendly mathematical modeling and simulation tool
for students, scientists and engineers. By Starsman Technologies, Inc.
• GALibrary is a Dynamic Link Library (DLL) for Microsoft Windows.
This library can be called from a Microsoft Windows programming
language such as Visual Basic, C/C++ (e.g., Microsoft, Borland),
SmallTalk for Windows or other language that supports calling DLLs. By
BioComp Systems, Inc.
• Domain Solutions, Inc. offers a library of neural network paradigms which
allow developers to add neural network capabilities to their applications.
This software is a C++ class library of proven neural network models.
• NeuralWorks Predict is a state-of-the-art development environment for
developing and deploying real-time applications in forecasting, modeling

9. and classification automatically. Instead, it lets you quickly prototype and
integrate neural network models into solutions that yield tomorrow's
performance today. This powerful system combines neural network
technology with fuzzy logic, statistics and genetic algorithms to identify
solutions. By NeuralWare, Inc.
• NeuroShell Easy Predictor - Designed to be extremely easy to use, this
product is used for forecasting and predicting numeric amounts like sales,
prices, workload, level, cost, scores, speed, capacity, etc. It contains two
of our newest proprietary algorithms (neural and genetic) with no
parameters for you to have to set. By Ward Systems Group, Inc.
Demonstrations
• Flying Circus:Online Java Demos at Evonet
• Conway's Life Game in Javascript. Cellular automata game (implemented
with JavaScript only). This version is based on the original Conway's life
game algorithm, but allows user to modify the rules by which the cells
evolve.
• Traveling Salesman.This applet uses evolutionary programming to solve
small traveling salesman problems. Source code is available on request.
• Java Demonstration of the Synchronization Task. The applet demonstrates
a cellular automaton (CA) evolved to solve the synchronization task. In
this task, the one-dimensional, binary-state CA, upon given any initial
configuration, must reach a final configuration, within a given number of
time steps, that oscillates between all 0s and all 1s on successive time
steps.
• Amoebas. This is the proof-of-concept program to a much
largerevolutionary sim. In this evolutionary sim, a group of aeomebas will
evolve to fit the environment that they are in, an environment that the user
configures. The more suitable the amoeba, the longer it lives and the better
chance it has to evolve.
• Bugs. On the muddy bottom of a pond a number of protozoa cruise around
eating dead bacteria which rain down from above. Their endless search for
food takes energy and those who do not find enough nourishment will die.
• Genetic Algorithm Toolkit. Environment for evolving walking techniques
in artificial creatures. The system uses genetic algorithms to evolve 2D,

10. vector graphic-based creature models. Creatures can be created using the
skeleton editor application (examples included).
• Floys - Artificial Life in Java. Floys are social, territorial artificial life
"animals"implemented in Java. eFloys are evolving FloysThey belong to
the flocking Alife creatures variety, sharing with them the social tendency
to stick together, and life-like behavior which is based on a few simple,
local rules.
• Convex Hull Graph Algorithm Demo. An applet demonstrates the speed
and technique between Quick Hull algorithm and Brute Force algorithm in
sloving the Convex Hull problem.
• Java Genetic Algorithm Package. The package banda.genalg contains Java
classes implementing a framework for genetic algorithms. The package is
fully extensible, allowing customization of nearly any aspect of the
genetic algorithm or the genotypes. It can also be used for genetic
programs and evolution programs.
• Java Travelling Salesman. Fast implementation of the TSP. LetS the user
draw her or his own cities.
• Virtual Arboretum
• GPsys is a Genetic Programming system developed inthe Java
programming language. This system provides complete documentation (in
javadoc format) and examples
• TSP. This is a demonstration of the Travelling Salesman Problem (TSP).
• Minimum Genetic Tree Finder Using Kruskal Algorithm. This is a cool
applet which shows how to solve the minimum genetic tree problem of a
graph using the Kruskal Algorithm.
• Order Projects By Deadlines Suppose n projects E(1),....E(n) are given.
For each and everyone of those projects there is a deadline d(i)>0 which is
an integer number of time units and a profit p(i)>0 which is gained only if
the project is fulfilled before the exceeded of the deadline.
• Minimum Rout Finder Using Dijkstra Algorithm This applet shows how
to find minimum routes of a Graph to reach a node from node 1 using the
Dijkstra Algorithm.
• Minimum Genetic Tree Finder Using Prim Algorithm And Adjoining
Array This cool applet shows how to solve the minimum genetic tree
problem of a graph using the Prim Algorithm and Adjoining Array

11. • FSA-GA Ants. Ants is a program to explore genetic programming and
learning.When Ants starts, you'll see a large green "food area" in the
center of the screen.
• Think Tank Games typically consist of some sort of competition between
opponents. Computer games have suffered from insufficiently intelligent
opponents since their early development
• Artificial termites is a demonstration of autonomous agents and an
example of simple artificial life. Each termite (red dot) has the same job,
to move the wood (yellow dots) into piles.
• Self Reproducing Cellular Automata Loops. A Java Cellular Automata
applet implementing some self-replicating and partially evolving loops - ie
very simple artificial life-forms. Also implements John Conway's Game of
Life rule
• Sample Genetic Algorithm function optimizer. Designed to be a tool to
teach about genetic algorithm (GA)-based optimization. Features
interactive, real-time control of GA parameters and visualization of the
optimization search process.
• GA Maze Solver is a configurable Genetic Algorithm which solves
Mazes, and Java/Genetic Algorithm Package (J/GAP), which is a class
library (Java package) for GA implemenations in Java .
• Genetic Algorithm Demo A graphical demonstration of a genetic
algorithm with the ability to dynamically change parameters. Also
includes a brief introduction to GAs.
• JavaScript Genetic Algorithm by J. J. Merelo, Geneura Team, Electronica
and Technologia of Computers Department, University of Granada, Spain.
This page includes the code for a full javascript Genetic Algorithm, which
is public domain
• Genetic programming in Java by Adaptive Systems group (ASG),
Department of Computer Science, Vrije Universiteit Brusell, Belgium.
• Simple symbolic Regression Applet
• Java toolbox based on GPC++
• Genetic algorithm demos from developer.com: The professional
developer's resource.
• NeuroGenetic Optimizer (NGO). As the name suggests, the NGO is a
neural network development tool that uses genetic algorithms to optimize
the inputs and structure of a neural network. NeuroGenetics is a new

12. technology that makes the development of neural networks easier and the
results more accurate. By BioComp Systems, Inc.
• Visual Basic GA String Matching Demo. In the demo a population of
strings evolves to match a target (upper case) string. The demo includes a
variety of graphical displays. Most parameters and displays can be
changed dynamically as the GA runs, click on things and see what
happens! By Centre for Communications Systems Research, University
College of London, UK.
• GAEiffel, a GA class library written in Eiffel. The library is based on bit-
string GAs, and incorporates both generational and steady-state
algorithms. The distribution includes a demonstration program for solving
some numerical minimization and maximization problems. Written and
submitted by I. M. Ikram, Computer Science Department, University of
Natal, Durban, South Africa.
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Web Pages Viewing in Google PageRank order View in alphabetical order
Genetic Algorithms Archive - http://www.aic.nrl.navy.mil/galist/
Archives of GA-List, the genetic algorithms mailing list. Hosted at the Navy Center for
Applied Research in Artificial Intelligence.
GAlib - http://lancet.mit.edu/ga/
A C++ library of genetic algorithm components. The library includes tools for using genetic
algorithms to do optimization in any C++ program using any representation and genetic
operators.
NeuroDimension Inc: Genetic Algorithm Software - http://www.nd.com
Use NeuroDimension's Genetic Server or Genetic Library products to embed genetic
algorithms into your own VB/C++ application.
Hellenic Complex Systems Laboratory - http://www.hcsl.com

13. An independent, nonprofit research laboratory involved in the transdisciplinary study of
complex systems. Invented the GA-based design of statistical quality control.
Genetic Java - http://www4.ncsu.edu/eos/users/d/dhloughl/public/stable.htm
A simple genetic algorithms applet with instructions and some sample problems.
Introduction to Genetic Algorithms with Java - http://cs.felk.cvut.cz/~xobitko/ga/
Introductory pages with interactive Java applets, useful tips for your own genetic algorithm
IlliGAL - http://www-illigal.ge.uiuc.edu/
Illinois Genetic Algorithms Laboratory at the University of Illinois at Urbana-Champaign.
Contains a large collection of technical reports and software.
International Society for Adaptive Behavior - http://www.isab.org/
ISAB is an international scientific society devoted to education and furthering research on
adaptive behavior in animals, animats, software agents, and robots.
SPHINcsX - http://www.stanford.edu/~buc/SPHINcsX/book.html
"Zeroth-Order Shape Optimization Utilizing a Learning Classifier System" Web-based
textbook.
GA Playground - http://www.aridolan.com/ga/gaa/gaa.html
A general GA toolkit implemented in Java, for experimenting with genetic algorithms and
handling optimization problems. Source code is available.
Open BEAGLE - http://www.gel.ulaval.ca/~beagle/
Open BEAGLE is an Evolutionary Computation (EC) framework entirely coded in C++. It
provides a software environment to do any kind of EC.
Cell Matrix Corporation - http://www.cellmatrix.com/entryway/entryway/core.html
Publications describe an application of their computer architecture to genetic algorithms.
Software includes an online circut simulator.
Hitch-Hiker's Guide to Evolutionary
Computation - http://www.cs.cmu.edu/Groups/AI/html/faqs/ai/genetic/top.html
Comprehensive FAQ for comp.ai.genetic. An unconventional and often witty introductory
compendium. ASCII text only.
Genetic and Evolutionary Algorithm Toolbox - http://www.geatbx.com/
GEATbx is a comprehensive implementation of evolutionary algorithms in Matlab. A broad
range of operators is fully integrated into one environment.
GECCO 2001 - http://www.isgec.org/GECCO-2001
Genetic and Evolutionary Computation Conference 2001, July 7-11: Holiday Inn in San
Francisco, California.
PC AI Genetic Algorithms - http://www.pcai.com/web/ai_info/genetic_algorithms.html
Contains links to genetic algorithms information on the Internet along with vendors and
references. Published by PC AI magazine.
Genetics-Based Machine Learning - http://manip.crhc.uiuc.edu/research.html
Generalization, scheduling and performance evaluation from the Teacher Research Group.
Introduction to Genetic Algorithms - http://www.rennard.org/alife/english/gavintrgb.html
An introductory explanation of genetic algorithms available in HTML and PDF formats. The
Genetic Algorithm Viewer Java applet shows the functioning of a genetic algorithm.
Lithos Evolutionary Computation - http://www.esatclear.ie/~rwallace/lithos.html
An evolutionary computation system using a stack-based virtual machine. Source code
available.
Genewood - http://www.genewood.host.sk/
Information on genetic algorithms, fuzzy logic, and artificial intelligence featuring
downloadable applications, source code, and links.
optiGA - http://www.optiwater.com/optiga.html
An ActiveX control for Genetic Algorithms written in Visual Basic. Provides a generic control
that will perform the genetic run for any optimization problem.

14. Bibliography on Genetic
Algrorithms - http://liinwww.ira.uka.de/bibliography/Ai/genetic.algorithms.html
Genetic algorithm citations starting with ICGA and FOGA. Part of the Computer Science
Bibliography Collection at the Universitat Karlsruhe in Germany.
Genetic Algorithm Experiment - http://www.oursland.net/projects/PopulationExperiment/
This Java applet demonstrates a continuous value genetic algorithm on a variety of problem
spaces with a variety of reproduction methods.
Genetic Algorithms for Squeak - http://www.consultar.com/Squeak/GA/
This GA framework in Squeak implements the operation of selection, mutation and
crossing-over with visualization features.
Papers by Lee Altenberg On-Line - http://dynamics.org/~altenber/PAPERS/
Research publications in mathematical population genetics, evolutionary computation, and
genetic algorithms.
GA-search - http://www.optiwater.com/GAsearch/
An advanced dedicated genetic algorithms search engine. The "Spider" indexes only GA
related sites.
rEvolutionaryEngineering - http://www.revolutionaryengineering.com/
Researches and develops applications using evolutionary algorithms and genetic algorithms
for finance and engineering.
Evolutionary Design of Neural Architectures - http://www.cs.iastate.edu/~gannadm/
Information, bibliography and resources on evolutionary synthesis of neuromorphic
systems. Maintained by the Artificial Intelligence Research Group at Iowa State University.
Netadelica Genetic Algorithms - http://www.netadelica.com/ga/
A brief experiment in coding a simple genetic algorithm 'bit counter' that compares different
evolution parameters.
The Biological Concept of Neoteny in Evolutionary Colour
Segmentation - http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_35.html
Genetic Algorithm to simulate neoteny, the retention by an organism of juvenile or even
larval traits into later life.
Crystal Ball Pro - http://www.cbpro.com
A global optimization and risk analysis software tool. Uses a unique combination of genetic
algorithms and neural networks.
Genetic Daemon - http://sourceforge.net/projects/geneticd/
An open source genetic engine server, capable to run any kind of genetic algorithm. It has
TCP architecture, working with software clients and human interaction.
Project Jeep - http://sourceforge.net/projects/jeepproject/
Jeep is a modular, abstract and distributed evolutionary programming core written in Java
(open source), allowing to grow autonomous agents as well a gene pool (as in genetic
algorithms).
Genetic Pattern Finder - http://www.foretrade.com/gpf.htm
Uses genetic algorithms to detect the best trading patterns and will adapt to any financial
data. The trading signales generated are statistically validated and can be easily exported.
GA-Walk! - http://www.gawalk.com/
A Java software system for evolving walking techniques in artificial skeletons using genetic
algorithms.
Musical Composition with Genetic
Algorithms - http://www.davidschoenberger.net/joy/research.html
Graduate research project of Joy Schoenberger at the College of William and Mary. It
attempts to use genetic algorithms for musical composition, with coherency through
genotype.

15. PPSN VI - http://www-rocq.inria.fr/fractales/PPSN2000/index.html
Sixth International Conference on Parallel Problem Solving from Nature (2000),
September 16-20: Paris, France.