darwin 0.4.1

A genetic/evolutionary algorithm library for Dart. Given a population of phenotypes, an evaluator (fitness function), and time, the algorithm will evolve the population until it crosses given fitness [...]

example/example.dart

import 'dart:async';
import 'dart:math';

import 'package:darwin/darwin.dart';

Future<Null> main() async {
  // Create first generation, either by random or by continuing with existing
  // progress.
  var firstGeneration =
      new Generation<MyPhenotype, bool, SingleObjectiveResult>()
        ..members
            .addAll(new List.generate(10, (_) => new MyPhenotype.Random()));

  // Evaluators take each phenotype and assign a fitness value to it according
  // to some fitness function.
  var evaluator = new MyEvaluator();

  // Breeders are in charge of creating new generations from previous ones (that
  // have been graded by the evaluator). Their only required argument is
  // a function that returns a blank phenotype.
  var breeder = new GenerationBreeder<MyPhenotype, bool, SingleObjectiveResult>(
      () => new MyPhenotype())
    ..crossoverPropability = 0.8;

  var algo = new GeneticAlgorithm<MyPhenotype, bool, SingleObjectiveResult>(
    firstGeneration,
    evaluator,
    breeder,
  );

  // Start the algorithm.
  await algo.runUntilDone();

  // Print all members of the last generation when done.
  algo.generations.last.members
      .forEach((Phenotype ph) => print("${ph.genesAsString}"));
}

Random random = new Random();

class MyEvaluator
    extends PhenotypeEvaluator<MyPhenotype, bool, SingleObjectiveResult> {
  Future<SingleObjectiveResult> evaluate(MyPhenotype phenotype) {
    // This implementation just counts false values - the more false values,
    // the worse outcome of the fitness function.
    final result = new SingleObjectiveResult();
    result.value =
        phenotype.genes.where((bool v) => v == false).length.toDouble();
    return new Future.value(result);
  }
}

class MyPhenotype extends Phenotype<bool, SingleObjectiveResult> {
  static int geneCount = 6;

  MyPhenotype();

  MyPhenotype.Random() {
    genes = new List<bool>(geneCount);
    for (int i = 0; i < geneCount; i++) {
      genes[i] = random.nextBool();
    }
  }

  bool mutateGene(bool gene, num strength) {
    return !gene;
  }
}