https://github.com/acupy/genetic-algorithm-es6

🔬 This package provides a framework for building applications where genetic algorithm (GA) is used for solving optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.
https://github.com/acupy/genetic-algorithm-es6

ai es6 framework genetic-algorithm population

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🔬 This package provides a framework for building applications where genetic algorithm (GA) is used for solving optimization problems based on a natural selection process that mimics biological evolution. The algorithm repeatedly modifies a population of individual solutions.

• Host: GitHub
• URL: https://github.com/acupy/genetic-algorithm-es6
• Owner: acupy
• License: mit
• Created: 2016-11-09T23:34:37.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2018-08-17T18:15:35.000Z (over 7 years ago)
• Last Synced: 2025-08-09T06:54:16.930Z (8 months ago)
• Topics: ai, es6, framework, genetic-algorithm, population
• Language: JavaScript
• Homepage: https://www.npmjs.com/package/genetic-algorithm-fw
• Size: 9.77 KB
• Stars: 9
• Watchers: 2
• Forks: 1
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Genetic algorithm framework built with JavaScript ES6

[![Build Status](https://travis-ci.org/acupy/genetic-algorithm-es6.svg?branch=master)](https://travis-ci.org/acupy/genetic-algorithm-es6)

[![NPM](https://nodei.co/npm/genetic-algorithm-fw.png?downloads=true)](https://npmjs.org/package/genetic-algorithm-fw)

This package provides a framework for building applications where genetic algorithms (GA) are used for solving optimization problems based on a natural selection process that mimics biological evolution.

The algorithm repeatedly modifies a population of individual solutions.

## Installation

```bash

npm install genetic-algorithm-fw

```

## Functions to define

#### mutation

```js

function mutation(oldPhenotype){

  // return a new phenotype

}

```

#### crossover

```js

function crossover (phenoTypeA, phenoTypeB) {

    // using phenoTypeA and phenotypeB create a new list of two phenoTypes

    // return [phenotype1, phenotype2]

}

```

#### fitness

```js

function fitness(phenotype) {

  // return the fitness number

  // the higher the value the fitter it is

}

```

#### competition

```js

function competition(phenoTypeA, phenoTypeB) {

    // return true when the fitness value is higher for phenoTypeA

    // otherwise return false

}

```

## Initialize GA object with the previously defined functions

```js

var GeneticAlgorithm = require('genetic-algorithm-fw');

var geneticalgorithm = new GeneticAlgorithm(

  mutation, // if not specified, no mutation happens

  crossover, // if not specified, the initial phenoTypes are returned

  fitness, // if not specified, 0 is returned

  competition, // if not specified, no competition happens

  [], // initial list of phenoTypes

  populationSize, // by defualt it is 100

  chanceOfMutation); // by defautlt it is 50

```

## Evolve our population

```js

// we can run as many iterations as we like

geneticalgorithm.evolve();

```

## Get the best result

```js

// we can check the best phenotype in our current population

var theBestPhenotype = geneticalgorithm.best();

```