evolutionary 

A (more) functional genetic algorithm library. Designed to make as few assumptions as possible about how you want your genetic algorithm library to run.

Warning: I wrote this in a day without tests in a kinda functional style. So it's probably buggy and not as performant as it could be. Use at your own risk.

Installing

npm install evolutionary

Usage

``js
var Evolutionary = require('evolutionary')
var Select1 = require('evolutionary/select1')
var Select2 = require('evolutionary/select2')

// Initialize it the way you want
var evolve = Evolutionary({
optimize: Math.max,
select1: Select1.bestOf2,
select2: Select2.bestOf2,
mutate: x => x,
crossover: (a, b) => [a, b],
seed: Math.random,
fitness: (a) => a * 2

populationSize: 250,
crossoverChance: 0.9,
mutateChance: 0.2,
fittestAlwaysSurvives: true
})

var population = evolve()
var nextPopulation = evolve(population)

// Population is sorted by fitness
var fittest = population[0]

// keep evolving to your heart's content
`

The only required option is seed and fitness. But this probably isn't going to be useful if you don't define at least mutate and crossover.

Options

$3

A function that returns a random individual when called.

$3

A function that accepts an individual and returns that individual's fitness score.

$3

A function that accepts two fitness scores, and returns whichever one is the better one. Defaults to Math.max.

$3

A function that accepts an individual, and returns a mutated version of that individual. Defaults to x => x, so you should probably change it.

$3

A function that accepts two parent individuals, and returns an array containing two children individuals created from those parents. Defaults to (a, b) => [a, b], so you should probably change it.

$3

A function that selects a single individual out of a population. See the Selection section below. Defaults to Select1.bestOf2.

$3

A function that selects a two individuals out of a population. See the Selection section below. Defaults to Select2.bestOf2.

$3

How many individuals there should be in the population. Defaults to 250.

$3

The chance for a crossover to happen. Defaults to 0.9.

$3

The chance for a mutation to happen. Defaults to 0.2.

$3

Whether or not the fittest individual should always move on to the next generation. Defaults to true.

Selection

There are a number of selection behaviors pre-written if the default isn't what you want.

`js
const Select1 = reqiure('evolutionary/select1')
const Select2 = reqiure('evolutionary/select2')
`

$3

Selects a random individual from the population.

$3

Picks two random individuals from the population, and selects the fitter one.

$3

Picks three random individuals from the population, and selects the fittest one.

$3

Picks n random individuals from the population, and selects the fittest one.

$3

Selects the fittest individual from the population.

$3

Selects two random individuals from the population.

$3

Selects two individuals using the Select1.bestOf2 selection behavior.

$3

Selects two individuals using the Select1.bestOf3 selection behavior.

$3

Selects two individuals using the Select1.bestOfN(n) selection behavior.

$3

Selects the fittest individual and a random individual from the population.

Generator

This package also includes a convenience function to wrap your genetic algorithm as a generator:

`
const Evolutionary = require('evolutionary')
const makeGenerator = require('evolutionary/generator')

const evolve = Evolutionary({ ... })
const done = (pop) => pop[0] == desiredSolution
const generator = makeGenerator(evolve, done)

generator.next()
generator.next()
generator.next()
generator.next()

// ...

``