private static void LaunchEvolutionRun(SolverParameters solverParameters, ExpressionGenerator <GameState> expressionGenerator)
{
var cache = new FitnessCache <GameAgentLogicGenome, double>(2000 * solverParameters.MaxEliteSize);
var evaluator = new GameAgentEvaluator <GameAgentLogicGenome>(cache, genome => new GameAgentWithLogic(genome));
var bellWeightedRandom = new CyclableBellWeightedRandom(10.0, 3.0, 1.0, 0.5, 0.1);
var genomeDescriptions = new GameAgentLogicGenomeDescription(bellWeightedRandom, expressionGenerator);
var defaultGenomeFactory = new GeneticSolver.Genome.DefaultGenomeFactory <GameAgentLogicGenome>(genomeDescriptions);
var mutationProbabilities = new Cyclable <double>(new[] { 0.3, 0.9, 1.0 });
var mutator = new GenomeMutator <GameAgentLogicGenome>(genomeDescriptions, mutationProbabilities, new UnWeightedRandom());
var logger = new GameAgentWithLogicSolverLogger();
var solver = new Solver <GameAgentLogicGenome, double>(
defaultGenomeFactory,
evaluator,
logger,
solverParameters,
new IEarlyStoppingCondition <GameAgentLogicGenome, double>[]
{
// new GeneticSolver.EarlyStoppingConditions.FitnessThresholdReachedEarlyStopCondition<GameAgentMultipliers, double>(fitness => fitness < 1e-6),
new GeneticSolver.EarlyStoppingConditions.ProgressStalledEarlyStoppingCondition <GameAgentLogicGenome, double>(10, 0.5, 0.8),
new GeneticSolver.EarlyStoppingConditions.FitnessNotImprovingEarlyStoppingCondition <GameAgentLogicGenome>(1, 100),
},
new IGenomeReproductionStrategy <GameAgentLogicGenome>[]
{
new CyclableReproductionStrategy <GameAgentLogicGenome>(new IGenomeReproductionStrategy <GameAgentLogicGenome>[]
{
new SexualGenomeReproductionStrategy <GameAgentLogicGenome, double>(mutator, new StratifiedBreedingStrategy(),
defaultGenomeFactory, genomeDescriptions, evaluator, 40, 2),
new SexualGenomeReproductionStrategy <GameAgentLogicGenome, double>(mutator, new GeneticSolver.PairingStrategies.RandomBreedingStrategy(),
defaultGenomeFactory, genomeDescriptions, evaluator, 40, 2),
new AsexualGenomeReproductionStrategy <GameAgentLogicGenome>(new GenomeMutator <GameAgentLogicGenome>(genomeDescriptions, 1.0, new UnWeightedRandom())),
}),
new AsexualGenomeReproductionStrategy <GameAgentLogicGenome>(new GenomeMutator <GameAgentLogicGenome>(genomeDescriptions, 0.4, new UnWeightedRandom())),
});
solver.NewGeneration += (s, e) => bellWeightedRandom.CycleStdDev();
solver.NewGeneration += (s, e) => mutationProbabilities.Cycle();
solver.NewGeneration += (sender, e) => cache.ClearExcept(e.OrderedGenomes.Select(g => g.GenomeInfo.Genome));
logger.Start();
var best = solver.Evolve(1000);
logger.LogGeneration(best);
logger.End();
}
/// <summary>
/// Allows the game to perform any initialization it needs to before starting to run.
/// This is where it can query for any required services and load any non-graphic
/// related content. Calling base.Initialize will enumerate through any components
/// and initialize them as well.
/// </summary>
protected override void Initialize()
{
// TODO: Add your initialization logic here
MAPWIDTH = 40;
MAPHEIGHT = 40;
sprites = TextureLoader.LoadTextures <Texture2D>(Content, "Sprites");
IMapGenStrat <DungeonMap> strat = new RandomRoomMapStrat <DungeonMap>(MAPWIDTH, MAPHEIGHT, 30, 1, 3, 10);
map = strat.CreateMap();
genomes = new List <Genome>();
mutator = new GenomeMutator();
for (int i = 0; i < POPULATION_SIZE; i++)
{
genomes.Add(new Genome(2500));
}
int genomeNumber = 1;
foreach (Genome g in genomes)
{
mutator.AddGenomeWalker(new GenomeWalker(map.GetStartTile().X, map.GetStartTile().Y, map, g, genomeNumber));
genomeNumber++;
}
Tile temp = map.GetRandomWalkable();
p = new Player(temp.X, temp.Y, ref map);
font = Content.Load <SpriteFont>("ASCII");
inputState = new InputState();
camera.ViewportWidth = graphics.GraphicsDevice.Viewport.Width;
camera.ViewportHeight = graphics.GraphicsDevice.Viewport.Height;
camera.CenterOn(temp);
start = map.GetRandomWalkable();
end = map.GetRandomWalkable();
base.Initialize();
}
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();
}
