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Genetic-multithread.js

Advanced genetic and evolutionary algorithm library written in Javascript by Sub Protocol.

Changes from original version

This is a modified genetic repo optimized for nodejs with multi thread performance optimization. it uses experimental feature worker_threads This version only supports genetic with node js, doesn't support running in browser.

requires to run program with node --experimental-worker

Examples

example in nodejs node --experimental-worker examples/nodeJsFitting.js

Install

npm install genetic-nodejs-multithread

Population Functions

The genetic-js interface exposes a few simple concepts and primitives, you just fill in the details/features you want to use.

Function Return Type Required Description
seed() Individual Yes Called to create an individual, can be of any type (int, float, string, array, object)
fitness(individual) Float Yes Computes a fitness score for an individual
mutate(individual) Individual Optional Called when an individual has been selected for mutation
crossover(mother, father) [Son, Daughter] Optional Called when two individuals are selected for mating. Two children should always returned
optimize(fitness, fitness) Boolean Yes Determines if the first fitness score is better than the second. See Optimizer section below
select1(population) Individual Yes See Selection section below
select2(population) Individual Optional Selects a pair of individuals from a population. Selection
generation(pop, gen, stats) Boolean Optional Called for each generation. Return false to terminate end algorithm (ie- if goal state is reached)
notification(pop, gen, stats, isFinished) Void Optional Runs in the calling context. All functions other than this one are run in a web worker.

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 Genetic.Optimize.Minimize would be used, as a smaller fitness score is indicative of better fit.

Optimizer Description
Genetic.Optimize.Minimizer The smaller fitness score of two individuals is best
Genetic.Optimize.Maximizer 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 (select1) operator. If both Single and Pair-wise operations are used (and if crossover is implemented) it is genetic.

Select Type Required Description
select1 (Single) Yes Selects a single individual for survival from a population
select2 (Pair-wise) Optional Selects two individuals from a population for mating/crossover

Selection Operators

Single Selectors Description
Genetic.Select1.Tournament2 Fittest of two random individuals
Genetic.Select1.Tournament3 Fittest of three random individuals
Genetic.Select1.Fittest Always selects the Fittest individual
Genetic.Select1.Random Randomly selects an individual
Genetic.Select1.RandomLinearRank Select random individual where probability is a linear function of rank
Genetic.Select1.Sequential Sequentially selects an individual
Pair-wise Selectors Description
Genetic.Select2.Tournament2 Pairs two individuals, each the best from a random pair
Genetic.Select2.Tournament3 Pairs two individuals, each the best from a random triplett
Genetic.Select2.Random Randomly pairs two individuals
Genetic.Select2.RandomLinearRank Pairs two individuals, each randomly selected from a linear rank
Genetic.Select2.Sequential Selects adjacent pairs
Genetic.Select2.FittestRandom Pairs the most fit individual with random individuals
var genetic = Genetic.create();

// more likely allows the most fit individuals to survive between generations
genetic.select1 = Genetic.Select1.RandomLinearRank;

// always mates the most fit individual with random individuals
genetic.select2 = Genetic.Select2.FittestRandom;

// ...

Configuration Parameters

Parameter Default Range/Type Description
size 250 Real Number Population size
crossover 0.9 [0.0, 1.0] Probability of crossover
mutation 0.2 [0.0, 1.0] Probability of mutation
iterations 100 Real Number Maximum number of iterations before finishing
fittestAlwaysSurvives true Boolean Prevents losing the best fit between generations
skip 0 Real Number Setting this higher throttles back how frequently genetic.notification gets called in the main thread.
workerPath '' String NodeJS only, set a custom fitness worker path
workersCount 0 number NodeJS only, set how many multi thread workers to use, set 0 to disable multi threading

Building

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

git clone git@github.com:subprotocol/genetic-js.git && make distcheck
Command Description
make Automatically install dev-dependencies

Contributing

Feel free to open issues and send pull-requests.

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