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Genetic Algorithms General Solver (GAGENES) is a C++ implementation of the geneticalgorithm concept.
This project is a complete cross-platform (Windows, Linux) framework for Evolutionary Computation in pure python. See the project site at http://pyevolve.sourceforge.net or the blog at http://pyevolve.sourceforge.net/wordpress
X-GAT (XML-based GeneticAlgorithm Toolkit) is a Java framework to optimize problems with Genetic Algorithms (GAs). Differently from other frameworks, X-GAT contains ready-to-use GAs implementations and new features can be easily added.
Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
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This library is a lightweight implementation of geneticalgorithm, contains the most popular types of chromosomes and the basic algorithms for selection, elitism, crossing and mutation.
Game Toolbox is a C# assembly designed to facilitate the creation of games and game prototypes. It contains no graphics code, does not depend on XNA, and is Mono-compatible. It provides implementations of A*, GOAP, a geneticalgorithm, and more.
Evolutionary Structural Optimization Package (ESOP) consists of software for viewing, analyzing, and optimizing structures containing beam, truss, and membrane plate elements utilizing OpenGL and the GeneticAlgorithm (GA). Created for use in M.S. theses
This is implementation of parallel geneticalgorithm with "ring" insular topology. Algorithm provides a dynamic choice of genetic operators in the evolution of. The library supports the 26 genetic operators. This is cross-platform GA written in С++.
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A .net implementation of a framework for genetic algorithms. This tool enables programmers to write the "core" of their problem and have a geneticalgorithm immediately setup for solving it.
gaKnn(GeneticAlgorithm Optimized K Nearest Neighbor Classification framework) is a frameowork for KNN optimization with a geneticalgorithm. The genetic algothm used for this is JGAP (http://jgap.sourceforge.net/).
Java API for implementing any kind of GeneticAlgorithm and Genetic Programming applications quickly and easily. Contains a wide range of ready-to-use GA and GP algorithms and operators to be plugged-in or extended. Includes Tutorials and Examples.
GEP is an evolutionary algorithm for function finding. This framework is a powerful way of expressing and coding genetic-like structures and quickly finding solutions through evolution by common genetic operators.
The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A geneticalgorithm and Markov simulations are currently implemented.
A concise example of the classical geneticalgorithm, with a fancy windows terminal display. Features DNA editing, save/load, customizable constraints and statistics logging.
A flexible programming library for evolutionary computation. Steady-state, generational and island model genetic algorithms are supported, using Darwinian, Lamarckian or Baldwinian evolution. Includes support for multiprocessor and distributed systems.