Nitroglycerin Pickle Machine

    genetic-js

    0.1.14 • Public • Published

    genetic-js

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

    Rational

    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!

    Examples

    Install

    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.

    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
    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.

    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, 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

    Contributing

    Feel free to open issues and send pull-requests.

    Install

    npm i genetic-js

    DownloadsWeekly Downloads

    42

    Version

    0.1.14

    License

    BSD

    Last publish

    Collaborators

    • subprotocol