public void TornamentTest()
{
var minGenes = 10;
var maxGenes = 20;
var geneset = CreateGeneSet();
var watch = Stopwatch.StartNew();
void FnDisplay(List <Rule> genes, int wins, int ties, int losses, int generation)
{
Console.WriteLine("-- generation {0} --", generation);
Display(
new Chromosome <Rule, Fitness>(genes, new Fitness(wins, ties, losses, genes.Count), Strategy.None),
watch);
}
var mutationRoundCounts = new List <int> {
1
};
var mutationOperators = new FnMutateDelegate[]
{
genes => MutateAdd(genes, geneset),
genes => MutateReplace(genes, geneset),
MutateRemove,
MutateSwapAdjacent,
MutateMove,
};
void FnMutate(List <Rule> genes) =>
Mutate(genes, x => new Fitness(0, 0, 0, 0), mutationOperators, mutationRoundCounts);
List <Rule> FnCrossover(IReadOnlyList <Rule> parent, IReadOnlyList <Rule> doner)
{
var child = parent.Take(parent.Count / 2).Concat(doner.Skip(doner.Count / 2)).ToList();
FnMutate(child);
return(child);
}
List <Rule> FnCreate() =>
Rand.RandomSample(geneset, Rand.Random.Next(minGenes, maxGenes));
int FnSortKey(List <Rule> genes, int wins, int ties, int losses) => - 1000 * losses - ties + 1 / genes.Count;
var unused =
Genetic <Rule, Fitness> .Tournament(FnCreate, FnCrossover, PlayOneOnOne, FnDisplay, FnSortKey, 13);
}
public void PerfectKnowledgeTest()
{
var minGenes = 10;
var maxGenes = 20;
var geneset = CreateGeneSet();
var watch = Stopwatch.StartNew();
void FnDisplay(Chromosome <Rule, Fitness> candidate, int?length) =>
Display(candidate, watch);
Fitness FnGetFitness(IReadOnlyList <Rule> genes) =>
GetFitness(genes);
var mutationRoundCounts = new List <int> {
1
};
var mutationOperators = new FnMutateDelegate[]
{
genes => MutateAdd(genes, geneset),
genes => MutateReplace(genes, geneset),
MutateRemove,
MutateSwapAdjacent,
MutateMove,
};
void FnMutate(List <Rule> genes) =>
Mutate(genes, FnGetFitness, mutationOperators, mutationRoundCounts);
List <Rule> FnCrossover(IReadOnlyList <Rule> parent, IReadOnlyList <Rule> doner)
{
var child = parent.Take(parent.Count / 2).Concat(doner.Skip(doner.Count / 2)).ToList();
FnMutate(child);
return(child);
}
List <Rule> FnCreate() =>
Rand.RandomSample(geneset, Rand.Random.Next(minGenes, maxGenes));
var optimalFitness = new Fitness(620, 120, 0, 11);
var best = Genetic <Rule, Fitness> .GetBest(FnGetFitness, minGenes, optimalFitness, null, FnDisplay, FnMutate,
FnCreate, 500, 20, FnCrossover);
Assert.IsTrue(optimalFitness.CompareTo(best.Fitness) <= 0);
}
