0.3.7 • Public • Published


    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


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


    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.


    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


    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


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

    git clone && make distcheck
    Command Description
    make Automatically install dev-dependencies


    Feel free to open issues and send pull-requests.


    npm i genetic-nodejs-multithread

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