EvoSynth (Evolutionary Computation Synthesizer) is a framework for rapid development and prototyping of evolutionary algorithms.
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== EvoSynth Website:: http://evosynth.rubyforge.org/ Project:: http://rubyforge.org/projects/evosynth/ Gem:: http://rubygems.org/gems/evosynth Sourcecode:: http://gitorious.org/evosynth and http://github.com/yadler/EvoSynth Author:: Yves Adler (http://confusedbits.net/, mailto:firstname.lastname@example.org) Copyright:: Copyright (c) 2009, 2010 Yves Adler <email@example.com> License:: MIT (see LICENSE) == Description EvoSynth (Evolutionary Computation Synthesizer) is a framework for rapid development and prototyping of evolutionary algorithms. == Installation * gem install evosynth (as root) * for detailed instructions (build from source, etc.) see INSTALL == Features (for details see docs/FEATURES) * classes for individuals, populations, algorithm configurations, genomes * support for custom randomizer * meta operators: proportional, sequentional and conditional combined operators * logging support with exporter to gnuplot, html and csv * many predefined fitness functions * benchmarking features (evobench module): * diversity calculations (distance, entropy and subsequence) * mean, median, variance calculations for array/population * t-test to determine statistical significance * Comparator, to compare the performance of two or more Evolvers * TestRun: runs a evolver with a given configuration (n times) and collects the produced data * Experiment class: run a experiment with a experimental plan and compare different parameters * FullFactorialPlan : full factorial experimental plan * most common evolutionary algorithms: * hillclimber (single individual and population based) * standard genetic algorithm, steady state GA * memetic algorithm * evolution strategies (adaptive, selfadaptive and derandomized) * local search (hillclimber, threshold acceptance, simulated annealing, great deluge, record-to-record travel) * coevolutionary algorithms (round robin and balanced) * selection strategies: * identity * random selection * best selection * n-stage tournament selection * tournament selection * fitness proportional selection * roulette wheel selection * mutations: * identity * one gene flipping, binary mutation, efficient binary mutation * exchange mutation, inversion mutation, mixing mutation, shifting mutation * uniform real mutation, gauss mutation, self-adaptive gaus mutation * recombinations: * identity * one-point-crossover, k-point-crossover, uniform crossover * arithmetic crossover * ordered recombination, partially mapped crossover, edge recombination * global uniform crossover, global arithmetic crossover