public GeneticAlgorithm(int _genome_size, IProcreator <T> _procreator, ISelector <T> _selector, IPopulationGenerator <T> _generator, Goal _goal, Random _random, string _name)
{
procreator = _procreator;
selector = _selector;
generator = _generator;
goal = _goal;
genome_size = _genome_size;
name = _name;
random = _random;
}
public void Step(int survivors, ISelector selector, IProcreator procreator, IEvaluator evaluator, Random random)
{
pooledPopulation.Clear();
for (int i = 0; i < survivors; i++)
{
pooledPopulation.Append().GetChromosome().AddRange(CurrentPopulation.ReadOnlyIndividuals[i].Chromosome);
}
selector.Initialize(CurrentPopulation);
while (pooledPopulation.Individuals.Count < CurrentPopulation.ReadOnlyIndividuals.Count)
{
procreator.Procreate(pooledPopulation.Append().GetChromosome(), selector, random);
}
pooledPopulation.Freeze(evaluator);
Population temp = (Population)CurrentPopulation;
CurrentPopulation = pooledPopulation;
GenerationCount++;
pooledPopulation = temp;
}
private void ensureInitialized()
{
if (precedenceTree == null)
{
precedenceTree = new PathTree(paths);
evaluator = new TravelCostEvaluator(precedenceTree);
selector = new RouletteWheelSelector();
IMutator mutator = new ReverseSequenceMutator();
procreator = new SexualProcreator(new ShortestTravelCrossover(precedenceTree), mutator, 0.5);
Population genesis = new Population();
Individual individual0 = genesis.Append();
IReadOnlyList <PathNode> nodes = precedenceTree.NearestNeighborTraversal();
List <int> chromosome0 = individual0.GetChromosome();
chromosome0.AddRange(nodes.Select(node => node.Id));
//chromosome0.AddRange(Enumerable.Range(0, precedenceTree.Nodes.Count));
evaluator.Baseline(chromosome0);
//List<int> lastChromosome = chromosome0;
for (int i = 1; i < 100; i++)
{
List <int> chromosome = genesis.Append().GetChromosome();
chromosome.AddRange(chromosome0);
//chromosome.AddRange(lastChromosome);
mutator.Mutate(chromosome, random);
//lastChromosome = chromosome;
}
genesis.Freeze(evaluator);
optimizer = new GeneticOptimizer(genesis);
maxFitness = double.MinValue;
optimalPriorities = new List <int>();
}
}
