Package Exports

genetic-search
genetic-search/es/index.js
genetic-search/lib/index.js

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Readme

Multiprocessing Genetic Algorithm Implementation for TypeScript

Overview

This project provides a TypeScript implementation of a genetic algorithm that can be used for optimization problems. The algorithm is designed to be highly customizable and can be used for a wide range of applications.

Features

Multiprocessing support: The algorithm can be run in parallel using multiple processes, making it suitable for large-scale optimization problems.
Deep customization: The algorithm can be customized by providing custom implementations for various components:
- base parameters (population size, survival rate, crossover rate);
- strategies (population, phenome, fitness, sorting, selection, mutation, crossover, caching);
- scheduler for dynamic tuning of all the macro parameters.
Separation of phenome metrics and fitness calculation: The algorithm separates the computation of phenomes and fitness values, allowing normalization and other operations to be applied to the phenomes of all genomes prior to evaluating the fitness function.
Type-safe: The project uses TypeScript, which provides type safety and helps catch errors at compile-time.

Setup

For single process (including browser apps) use:

npm i genetic-search

For multiprocessing in node environment use:

npm i genetic-search-multiprocess

Usage example

Let's get a max value of the parabola: y = -(x-12)^2 - 3.

import type {
  GeneticSearchConfig,
  GeneticSearchStrategyConfig,
} from "genetic-search";
import {
  GeneticSearch,
  SimplePhenomeCache,
  DescendingSortingStrategy,
  RandomSelectionStrategy,
} from "genetic-search";

const config: GeneticSearchConfig = {
  populationSize: 100,
  survivalRate: 0.5,
  crossoverRate: 0.5,
};

const strategies: GeneticSearchStrategyConfig<ParabolaArgumentGenome> = {
  populate: new ParabolaPopulateStrategy(),
  phenome: new ParabolaMultiprocessingPhenomeStrategy({
    poolSize: 4,
    task: async (data) => [-((data[0] - 12)**2) - 3],
    onTaskResult: () => void 0,
  }),
  fitness: new ParabolaMaxValueFitnessStrategy(),
  sorting: new DescendingSortingStrategy(),
  selection: new RandomSelectionStrategy(2),
  mutation: new ParabolaMutationStrategy(),
  crossover: new ParabolaCrossoverStrategy(),
  cache: new SimplePhenomeCache(),
}

const search = new GeneticSearch(config, strategies);

expect(search.partitions).toEqual([50, 25, 25]);

await search.fit({
  generationsCount: 100,
  beforeStep: () => void 0,
  afterStep: () => void 0,
});

const bestGenome = search.bestGenome;
console.log('Best genome:', bestGenome);

Strategies implementation:

import type {
  BaseGenome,
  BaseMutationStrategyConfig,
  CrossoverStrategyInterface,
  FitnessStrategyInterface,
  GenerationFitnessColumn,
  GenerationPhenomeMatrix,
  IdGeneratorInterface,
  PopulateStrategyInterface,
} from "genetic-search";
import type { MultiprocessingPhenomeStrategyConfig } from "genetic-search-multiprocess";
import { BaseMutationStrategy } from "genetic-search";
import { MultiprocessingPhenomeStrategyConfig } from "genetic-search-multiprocess";

export type ParabolaArgumentGenome = BaseGenome & {
  id: number;
  x: number;
}

export type ParabolaTaskConfig = [number];

export class ParabolaPopulateStrategy implements PopulateStrategyInterface<ParabolaArgumentGenome> {
  populate(size: number, idGenerator: IdGeneratorInterface<ParabolaArgumentGenome>): ParabolaArgumentGenome[] {
    const result: ParabolaArgumentGenome[] = [];
    for (let i=0; i<size; ++i) {
      result.push({ id: idGenerator.nextId(), x: Math.random() * 200 - 100 });
    }
    return result;
  }
}

export class ParabolaMutationStrategy extends BaseMutationStrategy<ParabolaArgumentGenome, BaseMutationStrategyConfig> {
  constructor() {
    super({ probability: 1 });
  }

  mutate(genome: ParabolaArgumentGenome, newGenomeId: number): ParabolaArgumentGenome {
    return { x: genome.x + Math.random() * 10 - 5, id: newGenomeId };
  }
}

export class ParabolaCrossoverStrategy implements CrossoverStrategyInterface<ParabolaArgumentGenome> {
  cross(lhs: ParabolaArgumentGenome, rhs: ParabolaArgumentGenome, newGenomeId: number): ParabolaArgumentGenome {
    return { x: (lhs.x + rhs.x) / 2, id: newGenomeId };
  }
}

export class ParabolaMultiprocessingPhenomeStrategy extends BaseMultiprocessingPhenomeStrategy<ParabolaArgumentGenome, MultiprocessingPhenomeStrategyConfig<ParabolaTaskConfig>, ParabolaTaskConfig> {
  protected createTaskInput(genome: ParabolaArgumentGenome): ParabolaTaskConfig {
    return [genome.x];
  }
}

export class ParabolaMaxValueFitnessStrategy implements FitnessStrategyInterface {
  score(results: GenerationPhenomeMatrix): GenerationFitnessColumn {
    return results.map((result) => result[0]);
  }
}

Api Reference

For detailed documentation and usage examples, please refer to:

Unit testing

npm i
npm run test

License

Genetic Search TS is licensed under the MIT License.