private void CalculateFitness()
{
foreach (var chromosome in _currentSession.CurrentPopulation.Where(x => x.FitnessScore == null))
{
chromosome.FitnessScore = _fitnessEvaluator.Evaluate(chromosome);
}
}
public IList <GeneScore> Run(IList <BaseChromosomeType> chromosomeTypes, OptimiserParameters parameters)
{
var allScores = new List <GeneScore>();
var exiting = false;
var iterationCount = 0;
var totalIterations = parameters.MaximumGenerations * parameters.PopulationSize;
var geneValidator = GetGeneValidator(chromosomeTypes, parameters.ParameterConditions);
var evolver = new Evolver(chromosomeTypes, parameters.Seed,
new CompositeGeneValidator(geneValidator, new OnceOnlyGeneValidator()));
evolver.ReportNoProgress += ReportNoProgress;
evolver.ReportNoProgress += delegate(object sender, CancelEventArgs args) { exiting |= args.Cancel; };
_fitnessEvaluator.Initialise(parameters);
IList <Gene> population = evolver.GetInitialPopulation(parameters.PopulationSize);
for (var generation = 0; generation < parameters.MaximumGenerations; generation++)
{
var scoresThisGeneration = new List <GeneScore>();
foreach (var gene in population)
{
var score = _fitnessEvaluator.Evaluate(gene);
scoresThisGeneration.Add(score);
iterationCount++;
if (OnIterationComplete(score, iterationCount, totalIterations - iterationCount))
{
exiting = true;
break;
}
}
scoresThisGeneration.Sort();
if (exiting)
{
break;
}
population = evolver.GetNextGeneration(parameters.ReproductionRate, parameters.MutationRate,
scoresThisGeneration.ConvertAll(score => score.Gene));
allScores.AddRange(scoresThisGeneration);
OnGenerationComplete(generation, scoresThisGeneration);
}
allScores.Sort();
OnComplete();
return(allScores);
}
