An implementation of Genetic Algorithms for Node JS
A Genetic Algorithm library designed for typescript
Library for solving genetic algorithm problems
Extra utilities for genetic-rs, including plotting and progress bars.
Sovereign biology engine for Quality-Diversity and Multi-Objective evolution.
A small framework for managing genetic algorithms
Commonly-used parts of genetic-rs
A genetic algorithm implementation for optimizing genetic algorithm parameters
genevo provides building blocks to run simulations of optimization and search problems using genetic algorithms (GA). Execute genetic algorithm (GA) simulations in a customizable and extensible way.
The optimization algorithms realized in Rust. In given time realized genetic and particle swarm algorithms.
Ratio's genetic algorithms library.
A genetic algorithm implementation
EW - optimization algorithms realized in Rust
A from-scratch genetic-algorithm library used in my march-madness-predictor project
The pragmaticgp gem provides a simple framework for building, running and managing genetic programming experiments which automatically discover algorithms and equations to solve user-defined problems.
A simple gem to help in the creation of genetic algorithms
== DESCRIPTION: Charlie is a library for genetic algorithms (GA) and genetic programming (GP). == FEATURES: - Quickly develop GAs by combining several parts (genotype, selection, crossover, mutation) provided by the library. - Sensible defaults are provided with any genotype, so often you only need to define a fitness function. - Easily replace any of the parts by your own code. - Test different strategies in GA, and generate reports comparing them. Example report: http://charlie.rubyforge.org/example_report.html == INSTALL: * sudo gem install charlie == EXAMPLES: This example solves a TSP problem (also quiz #142): N=5 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b} class TSP < PermutationGenotype(CITIES.size) def fitness d=0 (genes + [genes[0]]).each_cons(2){|a,b| a,b=CITIES[a],CITIES[b] d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 ) } -d # lower distance -> higher fitness. end use EdgeRecombinationCrossover, InversionMutator end Population.new(TSP,20).evolve_on_console(50) This example finds a polynomial which approximates cos(x) class Cos < TreeGenotype([proc{3*rand-1.5},:x], [:-@], [:+,:*,:-]) def fitness -[0,0.33,0.66,1].map{|x| (eval_genes(:x=>x) - Math.cos(x)).abs }.max end use TournamentSelection(4) end Population.new(Cos).evolve_on_console(500)
Framework for genetic algorithm fast development
unconventional genetics, aggressively metaprogrammed
Sample genetic program in Ruby
Genetica is a library to create and use Genetics Algorithms with Ruby.
Simple Framework of Genetic Algorithm
Providing an expressive DSL to illustrate genetic algorithm problems and sets of default methods.
Genetic Algorithm Pool Selection.
Evolve provides a simple and readable interface to make genetic algorithm optimization easy
Darwinning provides tools to build genetic algorithm solutions using a Gene, Organism, and Population structure.
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