A Node.js framework for implementing and testing genetic algorithms.
Genetic Algorithms Library
A modular JavaScript API for programming with genetic algorithms.
Implementation of genetic algorithms for nodejs and browser
Implementation of genetic algorithms for nodejs
Genetic algorithms
Genetic Algorithms 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
Genetic algorithms in js
Set of tools for optimizations based on genetic algorithms
Simple to use genetic algorithms library. Supports both NodeJS and web browsers.
A small library to help curious people to develop their own Genetic Algorithms.
Genetic algorithms in a given gene pool
An implementation of Genetic Algorithms for Node JS
CLI tool for evolving prompts using genetic algorithms
A simple lib to test genetic algorithms
GANN is a tool that helps developing neural networks that improve using genetic algorithms easier.
A module for Node-RED that adds a battery charging strategy to node-red-contrib-power-saver. It uses genetic algorithms to find the best schedule
Allows generating crosswords using genetic algorithms, based on a set of dictionaries of terms and definitions.
A library for quickly prototyping genetic algorithms for nodejs
Small framework for genetic algorithms
Lightweight Nodejs package for genetic algorithms <3
A module for Node-RED that adds a battery charging strategy to node-red-contrib-power-saver. It uses genetic algorithms to find the best schedule
Genetic algorithms auto-evolve trading strategies from 484+ factors. Crypto, A-shares, US stocks. Walk-forward validated.
Library for solving genetic algorithm problems
A parallelized genetic algorithm runner
A comprehensive collection of metaheuristic optimization algorithms implemented in Rust
Genetic algorithm tools library
A.I. genetic algorithm implementation
A Pragmatic Global Black-Box Optimizer
Dependency Structure Matrix Genetic Algorithm II with two-edge graphical linkage model
Library to implement genetic algorithms
Evolutionary dynamics on ternary strategy spaces
A generic, composable genetic algorithm framework for Rust
Proc-macro crate for the evolve genetic algorithm framework — provides the grammar! macro
Information-driven protein-protein docking using a genetic algorithm
This gem allows you to evolve chromosomes in order to solve 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
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.
Optimise anything that responds to 'fitness' and takes a hash
A simple library for implementing simple genetic algorithms.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.
No description provided.