public void Setup() { _chromosomeService = new ChromosomeService(); _populationService = new PopulationService(_chromosomeService); _matrixService = new MatrixService(new GraphConsistentService()); _sut = new EvolutionService(_chromosomeService); }
public EvolutionController(IValidatorFactory validatorFactory, IEvolutionService evolutionService) { _validatorFactory = validatorFactory; _evolutionService = evolutionService; }
public Algorithm(IEvolutionService evolutionService, IEnvironmentService environmentService) { var population = environmentService.GenerateInitialisedPopulation(75); // Evolve our population until we reach an near-optimal solution var generationCount = 0; var maxGenerations = 100000; var globalHighestScore = 0; var generationsWithNoChangeToScore = 0; var maxGenerationsWithNoChangeToScore = 400; // Simulate 'maxGenerations' amount of generations, unless the score hasn't changed for 'maxGenerationsWithNoChangeToScore'. while (generationCount < maxGenerations && generationsWithNoChangeToScore < maxGenerationsWithNoChangeToScore) { generationCount++; var scoreOfFittestIndividual = population.MostSuitableIndividualToProblem.SuitabilityScore; // If the score hasn't improved since the last generation -> if (scoreOfFittestIndividual == globalHighestScore) { // -> Increment 'generationsWithNoChangeToScore' by 1. // TODO: Check how many parts were high value assignments, // to influence the overwriting of better assignments generationsWithNoChangeToScore++; } else { // Reset 'generationsWithNoChangeToScore' to 0. generationsWithNoChangeToScore = 0; } // If the 'scoreOfFittestIndividual' is more than the 'globalHighestScore' -> if (scoreOfFittestIndividual > globalHighestScore) { // -> update the 'globalHighestScore' with the 'scoreOfFittestIndividual'. globalHighestScore = scoreOfFittestIndividual; } // [ Purpose: To log out data less frequently. // How: Sub-sample the data by logging an update every 'subSampleRate' generations. var subSampleRate = 10; if (generationCount % subSampleRate == 0) { Console.WriteLine($"Generation [{generationCount}] ~ Highest Fitness [{scoreOfFittestIndividual}]"); } population = evolutionService.EvolvePopulation(population); environmentService.UpdatePopulationSuitability(population); // ] } Console.WriteLine("\nSolution found!"); if (generationsWithNoChangeToScore == maxGenerationsWithNoChangeToScore) { Console.WriteLine( $"The score hasn't changed in {maxGenerationsWithNoChangeToScore} generations, so it has been considered an optimal solution."); } Console.WriteLine("\n\nGeneration: " + generationCount); Console.WriteLine("\n\nSolution\n--------"); Console.WriteLine(population.MostSuitableIndividualToProblem); Console.WriteLine("\nFitness: " + population.MostSuitableIndividualToProblem.SuitabilityScore); }