genetic-search
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Multiprocessing genetic algorithm implementation library
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JavaScript
"use strict";
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};
Object.defineProperty(exports, "__esModule", { value: true });
exports.ProportionalSelectionStrategy = exports.TournamentSelectionStrategy = exports.TruncationSelectionStrategy = exports.RandomSelectionStrategy = exports.DescendingSortingStrategy = exports.AscendingSortingStrategy = exports.ReferenceLossFitnessStrategy = exports.BasePhenomeStrategy = exports.BaseMutationStrategy = void 0;
var utils_1 = require("./utils");
var itertools_1 = require("./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
*/
var BaseMutationStrategy = /** @class */ (function () {
/**
* Constructs a new instance of the mutation strategy.
*
* @param config The configuration for the mutation strategy.
*/
function BaseMutationStrategy(config) {
this.config = config;
}
return BaseMutationStrategy;
}());
exports.BaseMutationStrategy = BaseMutationStrategy;
/**
* 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
*/
var BasePhenomeStrategy = /** @class */ (function () {
/**
* Constructs a new instance of the phenome strategy.
*
* @param config The configuration for the phenome strategy.
*/
function BasePhenomeStrategy(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.
*/
BasePhenomeStrategy.prototype.collect = function (population, cache) {
return __awaiter(this, void 0, void 0, function () {
var pairs, resultsMap, genomesToRun, newResults, _a, _b, _c, genome, result, _d, _e, _f, genome, result;
var e_1, _g, e_2, _h;
var _this = this;
return __generator(this, function (_j) {
switch (_j.label) {
case 0:
pairs = population.map(function (genome) { return [genome.id, cache.getReady(genome.id)]; });
resultsMap = new Map(pairs);
genomesToRun = population.filter(function (genome) { return resultsMap.get(genome.id) === undefined; });
return [4 /*yield*/, this.execTasks(genomesToRun.map(function (genome) { return _this.createTaskInput(genome); }))];
case 1:
newResults = _j.sent();
try {
for (_a = __values((0, itertools_1.zip)(genomesToRun, newResults)), _b = _a.next(); !_b.done; _b = _a.next()) {
_c = __read(_b.value, 2), genome = _c[0], result = _c[1];
cache.set(genome.id, result);
}
}
catch (e_1_1) { e_1 = { error: e_1_1 }; }
finally {
try {
if (_b && !_b.done && (_g = _a.return)) _g.call(_a);
}
finally { if (e_1) throw e_1.error; }
}
try {
for (_d = __values((0, itertools_1.zip)(genomesToRun, newResults)), _e = _d.next(); !_e.done; _e = _d.next()) {
_f = __read(_e.value, 2), genome = _f[0], result = _f[1];
resultsMap.set(genome.id, cache.get(genome.id, result));
}
}
catch (e_2_1) { e_2 = { error: e_2_1 }; }
finally {
try {
if (_e && !_e.done && (_h = _d.return)) _h.call(_d);
}
finally { if (e_2) throw e_2.error; }
}
return [2 /*return*/, population.map(function (genome) { return 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.
*/
BasePhenomeStrategy.prototype.execTasks = function (inputs) {
return __awaiter(this, void 0, void 0, function () {
var result, inputs_1, inputs_1_1, input, taskResult, e_3_1;
var e_3, _a;
var _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
result = [];
_d.label = 1;
case 1:
_d.trys.push([1, 6, 7, 8]);
inputs_1 = __values(inputs), inputs_1_1 = inputs_1.next();
_d.label = 2;
case 2:
if (!!inputs_1_1.done) return [3 /*break*/, 5];
input = inputs_1_1.value;
return [4 /*yield*/, this.config.task(input)];
case 3:
taskResult = _d.sent();
(_c = (_b = this.config).onTaskResult) === null || _c === void 0 ? void 0 : _c.call(_b, taskResult, input);
result.push(taskResult);
_d.label = 4;
case 4:
inputs_1_1 = inputs_1.next();
return [3 /*break*/, 2];
case 5: return [3 /*break*/, 8];
case 6:
e_3_1 = _d.sent();
e_3 = { error: e_3_1 };
return [3 /*break*/, 8];
case 7:
try {
if (inputs_1_1 && !inputs_1_1.done && (_a = inputs_1.return)) _a.call(inputs_1);
}
finally { if (e_3) throw e_3.error; }
return [7 /*endfinally*/];
case 8: return [2 /*return*/, result];
}
});
});
};
return BasePhenomeStrategy;
}());
exports.BasePhenomeStrategy = BasePhenomeStrategy;
/**
* 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
*/
var ReferenceLossFitnessStrategy = /** @class */ (function () {
/**
* Constructor of the reference loss fitness strategy.
*
* @param referenceConfig The configuration for the reference loss fitness strategy.
*/
function ReferenceLossFitnessStrategy(referenceConfig) {
this.referenceConfig = referenceConfig;
}
ReferenceLossFitnessStrategy.prototype.score = function (results) {
var normalizedLosses = this.formatLosses(results);
return normalizedLosses.map(function (x) { return -(0, utils_1.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.
*/
ReferenceLossFitnessStrategy.prototype.formatLosses = function (results) {
var _this = this;
return (0, utils_1.normalizePhenomeMatrix)(results, this.referenceConfig.reference).map(function (result) { return _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.
*/
ReferenceLossFitnessStrategy.prototype.weighRow = function (result) {
return (0, utils_1.arrayBinaryOperation)(result, this.referenceConfig.weights, function (x, y) { return x * y; });
};
return ReferenceLossFitnessStrategy;
}());
exports.ReferenceLossFitnessStrategy = ReferenceLossFitnessStrategy;
/**
* 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
*/
var AscendingSortingStrategy = /** @class */ (function () {
function 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.
*/
AscendingSortingStrategy.prototype.sort = function (input) {
return __spreadArray([], __read((0, itertools_1.sort)(input, function (lhs, rhs) { return lhs.fitness - rhs.fitness; })), false);
};
return AscendingSortingStrategy;
}());
exports.AscendingSortingStrategy = AscendingSortingStrategy;
/**
* 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
*/
var DescendingSortingStrategy = /** @class */ (function () {
function 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.
*/
DescendingSortingStrategy.prototype.sort = function (input) {
return __spreadArray([], __read((0, itertools_1.sort)(input, function (lhs, rhs) { return rhs.fitness - lhs.fitness; })), false);
};
return DescendingSortingStrategy;
}());
exports.DescendingSortingStrategy = DescendingSortingStrategy;
/**
* 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
*/
var RandomSelectionStrategy = /** @class */ (function () {
/**
* Constructor of the random selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
*/
function RandomSelectionStrategy(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.
*/
RandomSelectionStrategy.prototype.selectForCrossover = function (input, count) {
var result = [];
for (var i = 0; i < count; i++) {
var parents = [];
for (var j = 0; j < this.crossoverParentsCount; j++) {
parents.push((0, utils_1.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.
*/
RandomSelectionStrategy.prototype.selectForMutation = function (input, count) {
var result = [];
for (var i = 0; i < count; i++) {
result.push((0, utils_1.getRandomArrayItem)(input).genome);
}
return result;
};
return RandomSelectionStrategy;
}());
exports.RandomSelectionStrategy = RandomSelectionStrategy;
/**
* 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
*/
var TruncationSelectionStrategy = /** @class */ (function (_super) {
__extends(TruncationSelectionStrategy, _super);
/**
* 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.
*/
function TruncationSelectionStrategy(crossoverParentsCount, sliceThresholdRate) {
var _this = _super.call(this, crossoverParentsCount) || this;
_this.sliceThresholdRate = sliceThresholdRate;
return _this;
}
/**
* 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.
*/
TruncationSelectionStrategy.prototype.selectForCrossover = function (input, count) {
if (this.sliceThresholdRate) {
input = input.slice(0, input.length * this.sliceThresholdRate);
}
return _super.prototype.selectForCrossover.call(this, 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.
*/
TruncationSelectionStrategy.prototype.selectForMutation = function (input, count) {
if (this.sliceThresholdRate) {
input = input.slice(0, input.length * this.sliceThresholdRate);
}
return _super.prototype.selectForMutation.call(this, input, count);
};
return TruncationSelectionStrategy;
}(RandomSelectionStrategy));
exports.TruncationSelectionStrategy = TruncationSelectionStrategy;
/**
* 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
*/
var TournamentSelectionStrategy = /** @class */ (function () {
/**
* 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.
*/
function TournamentSelectionStrategy(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.
*/
TournamentSelectionStrategy.prototype.selectForCrossover = function (input, count) {
var result = [];
for (var i = 0; i < count; i++) {
var parents = [];
for (var 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.
*/
TournamentSelectionStrategy.prototype.selectForMutation = function (input, count) {
var result = [];
for (var 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.
*/
TournamentSelectionStrategy.prototype.runTournament = function (input) {
// Select random participants
var tournamentParticipants = [];
for (var k = 0; k < this.tournamentSize; k++) {
tournamentParticipants.push((0, utils_1.getRandomArrayItem)(input));
}
// Sort participants and select the best
tournamentParticipants.sort(function (lhs, rhs) { return rhs.fitness - lhs.fitness; });
return tournamentParticipants[0]; // Best participant
};
return TournamentSelectionStrategy;
}());
exports.TournamentSelectionStrategy = TournamentSelectionStrategy;
/**
* 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
*/
var ProportionalSelectionStrategy = /** @class */ (function () {
/**
* Constructor of the proportional selection strategy.
*
* @param crossoverParentsCount The number of parents to select for crossover.
*/
function ProportionalSelectionStrategy(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.
*/
ProportionalSelectionStrategy.prototype.selectForCrossover = function (input, count) {
var result = [];
// Calculate total fitness of the population
var totalFitness = input.reduce(function (acc, evaluatedGenome) { return acc + evaluatedGenome.fitness; }, 0);
for (var i = 0; i < count; i++) {
var parents = [];
for (var j = 0; j < this.crossoverParentsCount; j++) {
// Roulette wheel selection based on fitness proportion
var 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.
*/
ProportionalSelectionStrategy.prototype.selectForMutation = function (input, count) {
var result = [];
// Calculate total fitness of the population
var totalFitness = input.reduce(function (acc, evaluatedGenome) { return acc + evaluatedGenome.fitness; }, 0);
for (var i = 0; i < count; i++) {
// Roulette wheel selection based on fitness proportion
var 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`.
*/
ProportionalSelectionStrategy.prototype.selectByFitness = function (input, totalFitness) {
var e_4, _a;
var randomValue = Math.random() * totalFitness;
var result = input[input.length - 1]; // Fallback value in case of floating-point imprecisions
try {
for (var input_1 = __values(input), input_1_1 = input_1.next(); !input_1_1.done; input_1_1 = input_1.next()) {
var evaluatedGenome = input_1_1.value;
randomValue -= evaluatedGenome.fitness;
if (randomValue <= 0) {
result = evaluatedGenome;
break;
}
}
}
catch (e_4_1) { e_4 = { error: e_4_1 }; }
finally {
try {
if (input_1_1 && !input_1_1.done && (_a = input_1.return)) _a.call(input_1);
}
finally { if (e_4) throw e_4.error; }
}
return result;
};
return ProportionalSelectionStrategy;
}());
exports.ProportionalSelectionStrategy = ProportionalSelectionStrategy;
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