Implementation of genetic algorithms for nodejs
Multiprocessing genetic algorithm implementation library
Advanced genetic and evolutionary algorithm library
Implementation of genetic algorithms for nodejs and browser
Advanced genetic and evolutionary algorithm library (no web workers fork)
Genetic Algorithms Library
Multiprocessing genetic algorithm implementation library extension
Architecture-free neural network library with genetic algorithm implementations
Generate interactive visualizations of statistical genetic data
A modular JavaScript API for programming with genetic algorithms.
Genetic algorithm
Stop genetic optimization when stats is not fine
SDK for interacting with the GeneTrust privacy-preserving genetic data marketplace on Stacks/Bitcoin
Genetic programming helpers & strategies (tree based & multi-expression programming)
genetic engineering within JS
An Open Source Programming Language for Genetic Engineering
Implement Genetic Algorithm in JavaScript.
Foundation of algorithms and patterns taken and modified from here: http://burakkanber.com/blog/machine-learning-genetic-algorithms-in-javascript-part-2/#fiddle
General-purpose Genetic Algorithm library
A lightweight genetic algorithm library.
a general purpose genetic algorithm, an A.I. tool useful for machine evolution and adaptation
Genetic and evolutionary algorithms framework for the web
Genetika Kodo: A more genetic style of programming, you could call it genetic coding.
Neural Network using Genetic Algorithm
a little lib to use genetic algorithm
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
Main c++ algorith was developed in USB by electronic science departement, and then implemented to fit curves by B. Manuel, and finally convert to gem by CC.
Facilitates use of genetic evolution to find a solution to a defined problem.
This gem allows you to evolve chromosomes in order to solve problems
Simple genetic algorithm for functions minimization.
A Generic Algorithm Library for Ruby Language
Genetica is a library to create and use Genetics Algorithms with Ruby.
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
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