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• 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 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 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, 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.



• 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:
  • Graphical interface for input parameters.
    
  • Start, pause, continue the actual run.
    
  • Increase the max_generations limit when reached if you want to.
    
  • Graphical representation of adjusted fitness, structural complexity and tree depth.
    
  • Exploration of the individuals in the population: hits, depth, nodes, composition.
    
  • 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.
  • Sort the population depending on fitness, depth or nodes.
  • Easy upgrade from lil-gp files.
  • Independent kernel. User problems implemented in DLLs.
  • 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.



• 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.

• 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.
• FlexTool(ENM): Evolutionary Neuro Modeling Tool
• FlexTool(EFM): Evolutionary Fuzzy Modeling Tool
• 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.
  
  
• 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 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.

• 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, 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
  
  
• 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 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.