Skip to content


Folders and files

Last commit message
Last commit date

Latest commit



47 Commits

Repository files navigation


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


The existing Javascript GA/EP library landscape could collectively be summed up as, meh. All that I required to take over the world was a lightweight, performant, feature-rich, nodejs + browser compatible, unit tested, and easily hackable GA/EP library. Seamless Web Worker support would be the icing on my cake.

Until now, no such thing existed. Now you can have my cake, and optimize it too. Is it perfect? Probably. Regardless, this library is my gift to you.

Have fun optimizing all your optimizations!



npm install genetic-js

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.


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.Minimize The smaller fitness score of two individuals is best
Genetic.Optimize.Maximize The greater fitness score of two individuals is best


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
maxResults 100 Real Number The maximum number of best-fit results that webworkers will send per notification
webWorkers true Boolean Use Web Workers (when available)
skip 0 Real Number Setting this higher throttles back how frequently genetic.notification gets called in the main thread.


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

git clone && make distcheck
Command Description
make Automatically install dev-dependencies, builds project, places library to js/ folder
make check Runs test cases
make clean Removes files from js/ library
make distclean Removes both files from js/ library and dev-dependencies
make distcheck Equivlant to running make distclean && make && check


Feel free to open issues and send pull-requests.


Advanced genetic and evolutionary algorithm library written in Javascript







No releases published


No packages published