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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.
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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.
A concise example of the classical geneticalgorithm, with a fancy windows terminal display. Features DNA editing, save/load, customizable constraints and statistics logging.
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Galapagos is a GeneticAlgorithm framework written in Java 5 with the intended audience of undergraduates in an Artificial Intelligence class. The goal of Galapagos is usability: a competent student should be able to learn this library in an afternoon.
A Java implementation of the NEAT algorithm as created by Kenneth O Stanley. Also provides a toolkit for further experiments to be created and can provide both local and distributed learning environments.
NeuroDraughts is a Draughts/Checkers game that teaches itself how to play through self play. It combines an Artificial Neural Network, trained by Temporal Difference Learning using some GeneticAlgorithm style behaviour.
musicomp is a program which most important element is an evolutionary algorithm which uses data mining methods as a fitness function to generate monophone melodies.
unEvo is an Eclipse plug-in that provides support for the experimentation and research process on Evolutionary Algorithms, intended so that the user can implement an evolutionary algorithm without lost time in the code of the algorithm.
Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The
ga2 is a simple C++ library providing the necessary base classes to implement a geneticalgorithm in C++. It is based loosely on Goldberg's canonical GA, but with many modifications, improvements and additional features. Essentially feature-complete, and
A Visual Studio .NET C++ application can perform machine learning using geneticalgorithm, naive bayes, KNN, and Artificial Neural Networks (ANNs) read and processed from any standard ARFF.
Integer Singular Value Decomposition GeneticAlgorithm Function Fitter. This is an optimization algorithm that performs a similar role to a neural network.