genetic-search
Version:
Multiprocessing genetic algorithm implementation library
425 lines • 16 kB
JavaScript
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
import { normalizePhenomeMatrix, arrayBinaryOperation, arraySum, getRandomArrayItem } from "./utils";
import { sort, zip } from "./itertools";
/**
* Base class for mutation strategies.
*
* @template TGenome The type of genome objects in the population.
* @template TConfig The type of configuration for the mutation strategy.
*
* @category Strategies
* @category Mutation
*/
export class BaseMutationStrategy {
/**
* Constructs a new instance of the mutation strategy.
*
* @param config The configuration for the mutation strategy.
*/
constructor(config) {
this.config = config;
}
}
/**
* Base class for phenome strategies.
*
* @template TGenome The type of genome objects in the population.
* @template TConfig The type of configuration for the phenome strategy.
* @template TTaskConfig The type of configuration required to execute the task of the calculating phenome.
*
* @category Strategies
* @category Phenome
*/
export class BasePhenomeStrategy {
/**
* Constructs a new instance of the phenome strategy.
*
* @param config The configuration for the phenome strategy.
*/
constructor(config) {
this.config = config;
}
/**
* Collects and caches the phenome for a given population of genomes.
*
* @param population The population of genomes to collect phenome for.
* @param cache The cache used to store and retrieve phenomes.
* @returns A promise that resolves to a matrix of phenomes for the generation.
*/
collect(population, cache) {
return __awaiter(this, void 0, void 0, function* () {
const pairs = population.map((genome) => [genome.id, cache.getReady(genome.id)]);
const resultsMap = new Map(pairs);
const genomesToRun = population.filter((genome) => resultsMap.get(genome.id) === undefined);
const newResults = yield this.execTasks(genomesToRun.map((genome) => this.createTaskInput(genome)));
for (const [genome, result] of zip(genomesToRun, newResults)) {
cache.set(genome.id, result);
}
for (const [genome, result] of zip(genomesToRun, newResults)) {
resultsMap.set(genome.id, cache.get(genome.id, result));
}
return population.map((genome) => resultsMap.get(genome.id));
});
}
/**
* Executes the tasks to calculate the phenome of the given genomes.
*
* @param inputs The inputs for the tasks to execute.
* @returns A promise that resolves to an array of phenome for each genome.
*/
execTasks(inputs) {
return __awaiter(this, void 0, void 0, function* () {
var _a, _b;
const result = [];
for (const input of inputs) {
const taskResult = yield this.config.task(input);
(_b = (_a = this.config).onTaskResult) === null || _b === void 0 ? void 0 : _b.call(_a, taskResult, input);
result.push(taskResult);
}
return result;
});
}
}
/**
* A fitness strategy that calculates the fitness of a genome based on a reference loss.
*
* The fitness of a genome is calculated as the negative sum of the absolute differences between the reference loss
* and the loss calculated for the genome.
*
* @category Strategies
* @category Fitness
*/
export class ReferenceLossFitnessStrategy {
/**
* Constructor of the reference loss fitness strategy.
*
* @param referenceConfig The configuration for the reference loss fitness strategy.
*/
constructor(referenceConfig) {
this.referenceConfig = referenceConfig;
}
score(results) {
const normalizedLosses = this.formatLosses(results);
return normalizedLosses.map((x) => -arraySum(x));
}
/**
* Formats the losses by normalizing the phenome matrix and applying weights to each row.
*
* @param results The generation phenome matrix to format.
* @returns A matrix of formatted losses.
*/
formatLosses(results) {
return normalizePhenomeMatrix(results, this.referenceConfig.reference).map((result) => this.weighRow(result));
}
/**
* Weighs a row of phenome by multiplying each element with its corresponding weight.
*
* @param result The genome phenome row to weigh.
* @returns A genome phenome row with applied weights.
*/
weighRow(result) {
return arrayBinaryOperation(result, this.referenceConfig.weights, (x, y) => x * y);
}
}
/**
* Sorts a given iterable of genomes, fitness scores, and phenome rows in ascending order of their fitness scores.
*
* @param input An iterable containing tuples of genomes, their fitness scores, and their associated phenome rows.
* @returns An array of sorted tuples of genomes, fitness scores, and phenome rows.
*
* @category Strategies
* @category Sorting
*/
export class AscendingSortingStrategy {
/**
* Sorts a given iterable of genomes, fitness scores, and phenome rows in ascending order of their fitness scores.
*
* @param input An iterable containing tuples of genomes, their fitness scores, and their associated phenome rows.
* @returns An array of sorted tuples of genomes, fitness scores, and phenome rows.
*/
sort(input) {
return [...sort(input, (lhs, rhs) => lhs.fitness - rhs.fitness)];
}
}
/**
* Sorts a given iterable of genomes, fitness scores, and phenome rows in descending order of their fitness scores.
*
* @param input An iterable containing tuples of genomes, their fitness scores, and their associated phenome rows.
* @returns An array of sorted tuples of genomes, fitness scores, and phenome rows.
*
* @category Strategies
* @category Sorting
*/
export class DescendingSortingStrategy {
/**
* Sorts a given iterable of genomes, fitness scores, and phenome rows in descending order of their fitness scores.
*
* @param input An iterable containing tuples of genomes, their fitness scores, and their associated phenome rows.
* @returns An array of sorted tuples of genomes, fitness scores, and phenome rows.
*/
sort(input) {
return [...sort(input, (lhs, rhs) => rhs.fitness - lhs.fitness)];
}
}
/**
* A random selection strategy.
*
* This selection strategy randomly selects parents for mutation and crossover.
*
* @template TGenome The type of genome objects in the population.
*
* @category Strategies
* @category Selection
*/
export class RandomSelectionStrategy {
/**
* Constructor of the random selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
*/
constructor(crossoverParentsCount) {
this.crossoverParentsCount = crossoverParentsCount;
}
/**
* Selects parents for crossover.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents arrays.
*/
selectForCrossover(input, count) {
const result = [];
for (let i = 0; i < count; i++) {
const parents = [];
for (let j = 0; j < this.crossoverParentsCount; j++) {
parents.push(getRandomArrayItem(input).genome);
}
result.push(parents);
}
return result;
}
/**
* Selects parents for mutation.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents.
*/
selectForMutation(input, count) {
const result = [];
for (let i = 0; i < count; i++) {
result.push(getRandomArrayItem(input).genome);
}
return result;
}
}
/**
* A truncation selection strategy.
*
* This selection strategy selects the top `sliceThresholdRate` fraction of the population
* as parents for mutation and crossover.
*
* @template TGenome The type of genome objects in the population.
*
* @category Strategies
* @category Selection
*/
export class TruncationSelectionStrategy extends RandomSelectionStrategy {
/**
* Constructor of the truncation selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
* @param sliceThresholdRate The rate at which to slice the population.
*/
constructor(crossoverParentsCount, sliceThresholdRate) {
super(crossoverParentsCount);
this.sliceThresholdRate = sliceThresholdRate;
}
/**
* Selects parents for crossover.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents arrays.
*/
selectForCrossover(input, count) {
if (this.sliceThresholdRate) {
input = input.slice(0, input.length * this.sliceThresholdRate);
}
return super.selectForCrossover(input, count);
}
/**
* Selects parents for mutation.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents.
*/
selectForMutation(input, count) {
if (this.sliceThresholdRate) {
input = input.slice(0, input.length * this.sliceThresholdRate);
}
return super.selectForMutation(input, count);
}
}
/**
* A selection strategy that uses a tournament to select parents.
*
* This selection strategy runs a tournament between random participants from the population,
* and selects the best participant as a parent.
*
* @template TGenome The type of genome objects in the population.
*
* @category Strategies
* @category Selection
*/
export class TournamentSelectionStrategy {
/**
* Constructor of the tournament selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
* @param tournamentSize The number of participants in a tournament.
*/
constructor(crossoverParentsCount, tournamentSize) {
this.crossoverParentsCount = crossoverParentsCount;
this.tournamentSize = tournamentSize;
}
/**
* Selects parents for crossover.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents arrays.
*/
selectForCrossover(input, count) {
const result = [];
for (let i = 0; i < count; i++) {
const parents = [];
for (let j = 0; j < this.crossoverParentsCount; j++) {
// Select the best from the tournament
parents.push(this.runTournament(input).genome);
}
result.push(parents);
}
return result;
}
/**
* Selects parents for mutation.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents.
*/
selectForMutation(input, count) {
const result = [];
for (let i = 0; i < count; i++) {
// Select the best from the tournament
result.push(this.runTournament(input).genome);
}
return result;
}
/**
* Conducts a tournament and returns the best participant.
*
* @param input The population.
* @returns The best `EvaluatedGenome` from the tournament.
*/
runTournament(input) {
// Select random participants
const tournamentParticipants = [];
for (let k = 0; k < this.tournamentSize; k++) {
tournamentParticipants.push(getRandomArrayItem(input));
}
// Sort participants and select the best
tournamentParticipants.sort((lhs, rhs) => rhs.fitness - lhs.fitness);
return tournamentParticipants[0]; // Best participant
}
}
/**
* A proportional selection strategy.
*
* This selection strategy selects parents for mutation and crossover based on their fitness proportion.
*
* @template TGenome The type of genome objects in the population.
*
* @category Strategies
* @category Selection
*/
export class ProportionalSelectionStrategy {
/**
* Constructor of the proportional selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
*/
constructor(crossoverParentsCount) {
this.crossoverParentsCount = crossoverParentsCount;
}
/**
* Selects parents for crossover using proportional selection.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents arrays.
*/
selectForCrossover(input, count) {
const result = [];
// Calculate total fitness of the population
const totalFitness = input.reduce((acc, evaluatedGenome) => acc + evaluatedGenome.fitness, 0);
for (let i = 0; i < count; i++) {
const parents = [];
for (let j = 0; j < this.crossoverParentsCount; j++) {
// Roulette wheel selection based on fitness proportion
const selectedGenome = this.selectByFitness(input, totalFitness);
parents.push(selectedGenome.genome);
}
result.push(parents);
}
return result;
}
/**
* Selects parents for mutation using proportional selection.
*
* @param input The population extended with fitness scores and phenome to select parents from.
* @param count The number of parents to select.
* @returns An array of parents.
*/
selectForMutation(input, count) {
const result = [];
// Calculate total fitness of the population
const totalFitness = input.reduce((acc, evaluatedGenome) => acc + evaluatedGenome.fitness, 0);
for (let i = 0; i < count; i++) {
// Roulette wheel selection based on fitness proportion
const selectedGenome = this.selectByFitness(input, totalFitness);
result.push(selectedGenome.genome);
}
return result;
}
/**
* Helper method to select an individual based on fitness proportion.
*
* @param input The population.
* @param totalFitness The sum of all fitness scores in the population.
* @returns A selected `EvaluatedGenome`.
*/
selectByFitness(input, totalFitness) {
let randomValue = Math.random() * totalFitness;
let result = input[input.length - 1]; // Fallback value in case of floating-point imprecisions
for (const evaluatedGenome of input) {
randomValue -= evaluatedGenome.fitness;
if (randomValue <= 0) {
result = evaluatedGenome;
break;
}
}
return result;
}
}
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