public static void Main(string[] args)
{
// var solver = new Solver<Values.MyThing, int>(new DefaultGenomeFactory<MyThing>(), new MyThingGenomeEvaluator(), new MyThingGenomeDescription(), new MyThingSolverLogger(), new SolverParameters(1000));
var coefficientsToMatch = new Coefficients.Coefficients
{
FifthLevel = 0,
FourthLevel = 3.9,
ThirdLevel = -34.5,
SecondLevel = -9.8,
FirstLevel = 11.9
};
var pointsToMatch = Enumerable.Range(-1000, 1000).Select(x => new Point(x, coefficientsToMatch.Calc(x)));
var evaluator = new CoefficientsGenomeEvaluator(pointsToMatch);
var solverParameters = new SolverParameters(
1000,
2000,
0.3);
// var tasks = new List<Task>();
for (int i = 0; i < 10; i++)
{
// tasks.Add(Task.Run(() => LaunchEvolutionRun(genomeDescriptions, solverParameters, defaultGenomeFactory, evaluator)));
LaunchEvolutionRun(solverParameters, evaluator);
}
// Task.WaitAll(tasks.ToArray());
// TestBellCurve();
Console.WriteLine("Finished");
Console.ReadKey();
}
private static void LaunchEvolutionRun(SolverParameters solverParameters, CoefficientsGenomeEvaluator evaluator)
{
var mutationProbabilities = new Cyclable <double>(new [] { 0.3, 0.9, 1.0 });
var bellWeightedRandom = new CyclableBellWeightedRandom(10.0, 3.0, 1.0, 0.5, 0.1);
var genomeDescriptions = new CoefficientsGenomeDescriptions(bellWeightedRandom);
var defaultGenomeFactory = new DefaultGenomeFactory <Coefficients.Coefficients>(genomeDescriptions);
var mutator = new GenomeMutator <Coefficients.Coefficients>(genomeDescriptions, mutationProbabilities, new UnWeightedRandom());
var logger = new CoefficientsSolverLogger();
var solver = new Solver <Coefficients.Coefficients, double>(
defaultGenomeFactory,
evaluator,
logger,
solverParameters,
new IEarlyStoppingCondition <Coefficients.Coefficients, double>[]
{
new FitnessThresholdReachedEarlyStopCondition <Coefficients.Coefficients, double>(fitness => fitness < 1e-6),
new ProgressStalledEarlyStoppingCondition <Coefficients.Coefficients, double>(10, 0.5, 0.8),
new FitnessNotImprovingEarlyStoppingCondition <Coefficients.Coefficients>(1e-7, 10),
},
new IGenomeReproductionStrategy <Coefficients.Coefficients>[]
{
// new SexualGenomeReproductionStrategy<Coefficients.Coefficients, double>(mutator, new HaremBreedingStrategy(),
// defaultGenomeFactory, genomeDescriptions, evaluator, 100, 2),
new SexualGenomeReproductionStrategy <Coefficients.Coefficients, double>(mutator, new StratifiedBreedingStrategy(),
defaultGenomeFactory, genomeDescriptions, evaluator, 100, 2),
new SexualGenomeReproductionStrategy <Coefficients.Coefficients, double>(mutator, new RandomBreedingStrategy(),
defaultGenomeFactory, genomeDescriptions, evaluator, 100, 2),
// new AsexualGenomeReproductionStrategy<Coefficients.Coefficients>(mutator),
});
solver.NewGeneration += (s, e) => mutationProbabilities.Cycle();
solver.NewGeneration += (s, e) => bellWeightedRandom.CycleStdDev();
logger.Start();
var best = solver.Evolve(1000);
logger.LogGeneration(best);
logger.End();
}
