abstract-genetic-solver
A simple asynchronous genetic solver that's agnostic about genome types, fitness functions, etc.
Installation
npm i abstract-genetic-solver
Usage
Here's a trivial solver that treats each genome as an array of 10 floats, and tries to maximize their sum.
var Solver = require('abstract-genetic-solver')
var solver = new Solver(10)
// required methods that client must implement
solver.initGene = (index) => Math.random()
solver.mutateGene = (index, oldValue) => Math.random()
solver.measureFitness = async genome => genome.reduce((prev, val) => prev + val, 0)
// optional per-generation event
solver.afterGeneration = function () {
var best = solver.getCandidate(0)
console.log(`Best fitness so far: ${best.fitness}`)
console.log(`Best genome so far: ${best.genome}`)
}
// start solving
solver.paused = false
Note that measureFitness()
is async - this lets you calculate fitness values in a web worker, etc. The method can return a value synchronously of course, but it must be declared as async
.
Other settings
// number of individuals in each generation
solver.population = 100
// limit for simultaneous calls to measureFitness (0 => no limit)
solver.maxSimultaneousCalls = 0
// chances of an individual mutating or crossing over each generation
solver.mutationChance = 0.9
solver.crossoverChance = 0.3
// new generations can retain N fittest individuals from the previous
solver.keepFittestCandidates = 3
// How strongly to prefer fitter candidates when evolving
// 1 => choose from all candidates randomly
// 2 => strong bias towards fitter candidates
solver.rankSelectionBias = 1.5
Details
By Andy Hall, MIT license