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.

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
shouldContinue(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

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

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.

Contributing

Feel free to open issues and send pull-requests.