public void RealizeEvolution()
{
Random random = new Random();
Initialisation initialisation = new Initialisation(initialPopulationCount);
// Initialisation - validated
List <Path> population = initialisation.GenerateInitialPopulation();
for (int i = 0; i < population.Count; i++)
{
if (StaticOperations.ValidatePath(population[i]) == false)
{
throw new NotSupportedException();
}
}
// Evaluation
Evaluation evaluation = new Evaluation();
evaluation.EvaluatePopulation(population);
// Encoding
List <Representation> representations = new List <Representation>();
Decoder decoder = new Decoder();
foreach (Path path in population)
{
Representation representation = decoder.EncodePath(path);
representations.Add(representation);
}
// Evolution cycle
for (int i = 0; i < evolutionCycles; i++)
{
Console.Write("Epoch #" + i + ".");
// Reinitialisation happens every 1/10th iteration and randomly resets half of population
// Elite 10% is untouched by this process
if ((i % 500) == 0 && i != 0)
{
ReinitializePopulation(representations, (int)(3 * initialPopulationCount / 4));
}
// Remap fitness using exponential remapping
//RemapFitness(representations, remapParameter);
// Selection
Selection selection = new Selection(parentsCount, representations);
List <Representation> parents = selection.SelectParents();
//List<Representation> parents = selection.SelectCombinedParents();
// Genetic operator - validated
GeneticOperator geneticOperator = new GeneticOperator(descendantsCount, parents);
List <Representation> descendants = geneticOperator.GenerateDescendants();
// Decoding
List <Path> descendantPaths = new List <Path>();
foreach (Representation representation in descendants)
{
Path path = decoder.DecodeRepresentation(representation);
if (StaticOperations.ValidatePath(path) == false)
{
throw new NotSupportedException();
}
descendantPaths.Add(path);
}
// Evaluation
evaluation.EvaluatePopulation(descendantPaths);
for (int j = 0; j < descendants.Count; j++)
{
descendants[j].Fitness = descendantPaths[j].Fitness;
}
// Revaluate current population after fitness remapping
//List<Path> currentPaths = new List<Path>();
//foreach (Representation representation in representations)
//{
// Path path = decoder.DecodeRepresentation(representation);
// currentPaths.Add(path);
//}
//evaluation.EvaluatePopulation(currentPaths);
//for (int j = 0; j < representations.Count; j++)
//{
// representations[j].Fitness = currentPaths[j].Fitness;
//}
// Replacement
Replacement replacement = new Replacement(representations, descendants, initialPopulationCount);
//representations = replacement.GenerationReplacement();
//representations = replacement.NextGeneration();
representations = replacement.DuplicationElimination(7, representations.Count / 20 < 3 ? representations.Count / 20 : 3, 20);
Console.Write(" Maximum fitness: " + representations.Max(item => item.Fitness));
// Save to export file
SaveSixBestMembers(representations);
}
}