public void RandomSolutionShouldBeFeasible()
{
HeuristicsFactory.Register("SA", () => new SimulatedAnnealing());
MemeticAlgorithm algorithm = new MemeticAlgorithm();
Parameters parameters = new Parameters
{
PopulationSize = 30,
Selection = new RouletteSelection(),
CrossoverOperators = new List <CrossoverOperator>(),
MutationOperators = new List <MutationOperator>(),
CrossoverProbability = 0.75f,
MutationProbability = 0.05f,
Fitness = new FitnessFunction(4.0f, 1.0f),
ChromosomeFactory = new SolutionFactory(),
EliteChildrenCount = 3,
GeneCount = problem.Customers.Count,
FitnessStrategy = MemeticLibrary.Fitness.FitnessStrategy.MINIMIZE,
ConvergenceLimit = 1.0f,
PreservedChromosomesNumber = 10,
MaxIterations = 5000
};
parameters.CrossoverOperators.Add(new PartiallyMatchedCrossover());
parameters.MutationOperators.Add(new SwapOperator());
algorithm.Run(problem, parameters);
}
private void Register()
{
SelectionFactory.Register("roulette", () => new RouletteSelection());
CrossoverOperatorFactory.Register("OX", () => new OrderCrossover());
CrossoverOperatorFactory.Register("CX", () => new CycleCrossover());
CrossoverOperatorFactory.Register("PMX", () => new PartiallyMatchedCrossover());
CrossoverOperatorFactory.Register("UOX", () => new UniformBasedOrderCrossover());
MutationOperatorFactory.Register("swap", () => new SwapOperator());
MutationOperatorFactory.Register("insertion", () => new InsertionOperator());
MutationOperatorFactory.Register("inversion", () => new InversionOperator());
MutationOperatorFactory.Register("displacement", () => new DisplacementOperator());
HeuristicsFactory.Register("SA", () => new SimulatedAnnealing());
}
