public FitnessFunction(ChromosomeProcessor processor, FitnessEvaluator evaluator, ShiftMatrix matrix) { this.processor = processor; this.evaluator = evaluator; this.matrix = matrix; }
/// <summary> /// Creates a population and let it evolve until termination condition is reached. /// </summary> /// <param name="terminationCondition">Evolution termination condition evaluated on each new epoch</param> /// <returns>The best solution found across the evolution</returns> public ISolution EvolveUntil(TerminationCondition terminationCondition) { var rnd = RandomProvider.GetThreadRandom(); var shiftMatrix = MatrixBuilder.Build(this.problem); var enumerator = new IncreasingRowsRandomColumns(this.problem.Days, this.problem.Slots); var constraints = ConstraintsBuilder.Build(this.problem); var chromoProcessor = new ChromosomeProcessor(shiftMatrix, enumerator, constraints); var evaluator = new FitnessEvaluator(problem); var fitnessFunction = new Fitness.FitnessFunction(chromoProcessor, evaluator, shiftMatrix); var chromosomeLength = shiftMatrix.GetNumberOfUnforcedSlots(); const double crossoverProbability = 0.90; const double mutationProbability = 0.05; const int elitismPercentage = 5; epochs = 1; epochsWithoutFitnessImprovement = 0; overallBestFitness = -1; log.Debug("Starting population."); var population = new Population(); for (var i = 0; i < populationSize; i++) { var c = new Double[chromosomeLength]; for (var k = 0; k < chromosomeLength; k++) { c[k] = rnd.NextDouble(); } var ch = new Chromosome(c); population.Solutions.Add(ch); } //create the genetic operators var elite = new Elite(elitismPercentage); var crossover = new Crossover(crossoverProbability, true) { CrossoverType = CrossoverType.SinglePoint }; var mutation = new SwapMutate(mutationProbability); //create the GA itself var ga = new GeneticAlgorithm(population, fitnessFunction.Evaluate); //subscribe to the GAs Generation Complete event ga.OnGenerationComplete += Ga_OnGenerationComplete; //add the operators to the ga process pipeline ga.Operators.Add(elite); ga.Operators.Add(crossover); ga.Operators.Add(mutation); //run the GA ga.Run((pop, currentGeneration, currentEvaluation) => { return(terminationCondition(currentGeneration, epochsWithoutFitnessImprovement, population.MaximumFitness, population.AverageFitness)); }); population.GetTop(1)[0].Evaluate(fitnessFunction.Evaluate); return(SolutionBuilder.Build(overallBestFitness, shiftMatrix, epochs * population.PopulationSize, this.problem.Items)); }