@glavin001/genetic-js v0.2.1

Genetic.js

Advanced genetic and evolutionary algorithm library written in TypeScript. Special thanks to Sub Protocol for writing the intial JavaScript version.

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!

Install

npm install @glavin001/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.

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.

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.

Selection Operators

import Genetic from "@glavin001/genetic-js";

//
type Entity = string;
type UserData = {
  solution: string;
};

// Extend the abstract class Genetic.Genetic
class CustomGenetic extends Genetic.Genetic<Entity, UserData> {
    // more likely allows the most fit individuals to survive between generations
    public select1 = Genetic.Select1.RandomLinearRank;
    // always mates the most fit individual with random individuals
    public select2 = Genetic.Select2.FittestRandom;
    // ...
    public notification({
        population: pop,
        isFinished,
      }: {
        population: Population<Entity>;
        generation: number;
        stats: Stats;
        isFinished: boolean;
      }) {
        if (isFinished) {
            console.log(`Solution is ${pop[0].entity} (expected ${this.userData.solution})`);
        }
      }
}
// ...
const userData: UserData = {
    solution: "thisisthesolution",
};
const config: Partial<Genetic.Configuration> = {
    crossover: 0.4,
    iterations: 2000,
    mutation: 0.3,
    size: 20,
};
// ...
const genetic = new CustomGenetic(config, userData);
genetic.evolve();

Configuration Parameters

Contributing

Feel free to open issues and send pull-requests.