EvJs

Advanced genetic and evolutionary algorithm library written in Javascript by notVitaliy.

Install

yarn add evjs

How To

import { EvJs } from 'evjs'

const seed = () => {
  // Seed code here
}
const fitness = () => {
  // calculate fitness score
}
const mutate = () => {
  // mutate an individuals param(s)
}
const mate = () => {
  // breed 2 individuals
}

const evjsConfig = {
  notification: 0.5 // emit 50% of the logs
}

const generationConfig = {
  size: 10,
  crossover: 0.7,
  mutation: 0.4,
  keepFittest: true,
  select: 'random',
  pair: 'tournament2',
  optimizeKey: 'Max'
}

const individualConfig = {
  fitness,
  mutate,
  mate
}

const config = Object.assign({}, evjsConfig, generationConfig, individualConfig)

const evjs = new EvJs(config)

evjs.populate(seed)
evjs.run()

Generation Configuration Parameters

interface GenerationConfig {
  size?: number
  crossover?: number
  mutation?: number
  keepFittest?: boolean
  optimizeKey?: 'Max' | 'Min'
  select: string
  selectN?: number
  pair?: string
}

Parameter	Default	Range/Type	Description
size	250	Number	Population size
crossover	0.9	[0.0, 1.0]	Probability of crossover/breeding
mutation	0.2	[0.0, 1.0]	Probability of mutation
iterations	100	Real Number	Maximum number of iterations before finishing
keepFittest	true	Boolean	Prevents losing the best fit between generations
optimizeKey	Max	[Max, Min]	Optimization method to use
select	N/A	SelectType	Generation->mutate select type to use
pair	N/A	SelectType	Generation->breed select type to use

SelectType

Selectors	Description
Tournament{N}	Fittest of N random individuals
Fittest	Always selects the Fittest individual
Random	Randomly selects an individual

Optimizer

The optimizer specifies how to rank individuals against each other based on an arbitrary fitness score. For example, minimizing the sum of squared error for a regression curve Min would be used, as a smaller fitness score is indicative of better fit.

optimizeKey	Description
Min	The smaller fitness score of two individuals is best
Max	The greater fitness score of two individuals is best

Selection

An algorithm can be either genetic or evolutionary depending on which selection operations are used. An algorithm is evolutionary if it only uses a Single SelectType. If both Single and Pair-wise operations are used (and if crossover is implemented) it is genetic.

Select Type	Required	Description
select (Single)	Yes	Selects a single individual for survival from a population
pair (Pair-wise)	Optional	Selects two individuals from a population for mating/crossover

Individual Configuration Parameters

interface IndividualConfig {
  fitness: (entity: any): number
  mutate: (entity: any): any
  mate: (mother: any, father: any): [any, any]
}

Parameter	Type	Description
fitness	Function	Calculates the fitness score of an individual
mutate	Function	Mutates an individual
mate	Function	Mates 2 individuals and returns 2 new individuals

Building

To clone, build, and test Genetic.js issue the following command:

git clone [email protected]:notvitaliy/evjs.git

Command	Description
yarn	Automatically install dev-dependencies
npm test	Run unit tests

Contributing

Feel free to open issues and send pull-requests.