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);
}
public void FitnessToStringTest()
{
var fitness = new Fitness(10, 20, 30, 100);
Assert.AreEqual("50.0% Losses (30), 33.3% Ties (20), 16.7% Wins (10), 100 rules", fitness.ToString());
}
