private IList <CheckersMove> GetNewMoves()
{
InitializeGA();
var termination = new GenerationNumberTermination(GA.GenerationsNumber + m_generationNumber);
GA.Termination = termination;
Debug.Log("Running GA...");
if (GA.State == GeneticAlgorithmState.NotStarted)
{
GA.Start();
}
else
{
GA.Resume();
}
Debug.Log("Fitness: " + GA.BestChromosome.Fitness);
Debug.Log("Generations: " + GA.GenerationsNumber);
if (GA.BestChromosome.Fitness <= 0)
{
Debug.LogError("Lower than zero.");
}
if (GA.BestChromosome.Fitness == 0)
{
HudController.IsGameOver = true;
}
return((GA.BestChromosome as CheckersChromosome).Moves);
}
public void HasReached_GenerationNumberLowerThanCondition_False()
{
var repository = new MockRepository();
var ga = repository.StrictMock <IGeneticAlgorithm>();
using (repository.Ordered())
{
ga.Expect(g => g.GenerationsNumber).Return(1);
ga.Expect(g => g.GenerationsNumber).Return(2);
ga.Expect(g => g.GenerationsNumber).Return(3);
ga.Expect(g => g.GenerationsNumber).Return(4);
ga.Expect(g => g.GenerationsNumber).Return(5);
ga.Expect(g => g.GenerationsNumber).Return(6);
ga.Expect(g => g.GenerationsNumber).Return(7);
ga.Expect(g => g.GenerationsNumber).Return(8);
ga.Expect(g => g.GenerationsNumber).Return(0);
}
repository.ReplayAll();
var target = new GenerationNumberTermination(10);
Assert.IsFalse(target.HasReached(ga));
for (int i = 0; i < 8; i++)
{
Assert.IsFalse(target.HasReached(ga));
}
}
public GeneticSharpSolver Setup()
{
var chromosome = new FloatingPointChromosome(
_decisions.Select(d => d.LowerBound).ToArray(),
_decisions.Select(d => d.UpperBound).ToArray(),
_decisions.Select(d => d.TotalBits).ToArray(),
_decisions.Select(d => d.FractionDigits).ToArray());
var population = new Population(_populationSize, _populationSize + _offspringNumber, chromosome);
var fitness = new FuncFitness((c) => { return(_objective(c as FloatingPointChromosome, this)); });
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var termination = new GenerationNumberTermination(_maxGenerations);
_ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
_ga.Termination = termination;
if (_generationCallback != null)
{
_ga.GenerationRan += (sender, e) => _generationCallback(_ga);
}
return(this);
}
public static GeneticSharp.Domain.GeneticAlgorithm MakeDefault(Func <double[], double> func, Tuple <double, double>[] minmax)
{
var population = new Population(20, 40, chromosome.CreateFloatChromosone(minmax));
var fitness = new FuncFitness((c) =>
{
var fc = c as FloatingPointChromosome;
var values = fc.ToFloatingPoints();
return(func(values));
});
var selection = new RouletteWheelSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new FlipBitMutation();
var ga = new GeneticSharp.Domain.GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
var termination = new GenerationNumberTermination(1000);
ga.Termination = termination;
Console.WriteLine("Generation: (x1, y1), (x2, y2) = distance");
return(ga);
}
static void Main()
{
Application.EnableVisualStyles();
Application.SetCompatibleTextRenderingDefault(false);
//Application.Run(new Painter());
//setupNext();
var chromosome = new FloatingPointChromosome(
//lvl, hp, food, ruletype, rule, seed, tempo, pitch, r1, r2, r3
new double[] { 0, 0, 0, 7, 30, 32, 23, 23, 0, 0, 0 }, //min values
new double[] { 50, 500, 5, 1800, 999999999, 9999999, 288, 72, 999, 999, 999 }, //max values
new int[] { 6, 9, 3, 11, 34, 27, 9, 7, 10, 10, 10 }, //bits required for values
new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 } //bits for fractals
);
var population = new Population(5, 25, chromosome); //min-max population
var fitness = new myFitness();
var selection = new EliteSelection();
var crossover = new TwoPointCrossover();
var mutation = new FlipBitMutation();
var termination = new GenerationNumberTermination(10);
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
ga.Start();
}
/// <summary>
/// Initializes a new instance of the <see cref="GeneticSharp.Domain.GeneticAlgorithm"/> class.
/// </summary>
/// <param name="population">The chromosomes population.</param>
/// <param name="fitness">The fitness evaluation function.</param>
/// <param name="selection">The selection operator.</param>
/// <param name="crossover">The crossover operator.</param>
/// <param name="mutation">The mutation operator.</param>
public GeneticAlgorithm(
IPopulation population,
IFitness fitness,
ISelection selection,
ICrossover crossover,
IMutation mutation)
{
ExceptionHelper.ThrowIfNull("population", population);
ExceptionHelper.ThrowIfNull("fitness", fitness);
ExceptionHelper.ThrowIfNull("selection", selection);
ExceptionHelper.ThrowIfNull("crossover", crossover);
ExceptionHelper.ThrowIfNull("mutation", mutation);
Population = population;
Fitness = fitness;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Reinsertion = new ElitistReinsertion();
Termination = new GenerationNumberTermination(1);
CrossoverProbability = DefaultCrossoverProbability;
MutationProbability = DefaultMutationProbability;
TimeEvolving = TimeSpan.Zero;
State = GeneticAlgorithmState.NotStarted;
TaskExecutor = new LinearTaskExecutor();
}
public ITermination GenerationNumber()
{
var target = new GenerationNumberTermination(1);
var ga = Substitute.For <IGeneticAlgorithm>();
ga.GenerationsNumber.Returns(1);
target.HasReached(ga);
return(target);
}
public void OptimizeAI()
{
if (frequency != turnsSinceLastOptimization)
{
turnsSinceLastOptimization++;
return;
}
GenerateScoreData();
int maxSize = GetMaxSize();
AiSettingsChromosome intelligenceChromosome = new AiSettingsChromosome(3);
Population population = new Population(maxSize, maxSize, intelligenceChromosome);
FuncFitness fitness;
if (HistoryManagr.Instance.savingHistory == true)
{
fitness = new FuncFitness((c) => { return(GetHistoryFitness(c)); });
}
else
{
fitness = new FuncFitness((c) => { return(GetFitness(c)); });
}
var selection = new EliteSelection();
var crossover = new UniformCrossover(0.5f);
var mutation = new DisplacementMutation();
var termination = new GenerationNumberTermination(2);
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
List <IChromosome> savedChromosomes = new List <IChromosome>();
ga.Termination = termination;
ga.GenerationRan += (sender, e) =>
{
if (ga.GenerationsNumber == 2)
{
savedChromosomes = (List <IChromosome>)ga.Population.CurrentGeneration.Chromosomes;
}
};
ga.Start();
turnsSinceLastOptimization = 0;
settingsScoreData.Clear();
settingsData.Clear();
SetGeneratedData(savedChromosomes);
foreach (PlayerData player in PlayerManager.Instance.players)
{
EventSaverManager.Instance.SaveDDAEvolutionAIEvent(player.id);
}
}
public void HasReached_GenerationNumberGreaterOrEqualThanCondition_True()
{
var ga = Substitute.For <IGeneticAlgorithm>();
ga.GenerationsNumber.Returns(10, 11);
var target = new GenerationNumberTermination(10);
Assert.IsTrue(target.HasReached(ga));
Assert.IsTrue(target.HasReached(ga));
}
public void HasReached_Returns_True_WhenGenerationCountIsLargerThanMaximalNumberOfGenerations()
{
var sot = new GenerationNumberTermination(10);
var state = new GenerationCountState();
for (var i = 0; i < 10; i++)
{
Assert.False(sot.HasReached(null, state));
state.UpdateOnGenerationRan(null);
}
Assert.True(sot.HasReached(null, state));
}
public void HasReached_GenerationNumberLowerThanCondition_False()
{
var ga = Substitute.For <IGeneticAlgorithm>();
ga.GenerationsNumber.Returns(1, 2, 3, 4, 5, 6, 7, 8, 0);
var target = new GenerationNumberTermination(10);
Assert.IsFalse(target.HasReached(ga));
for (int i = 0; i < 8; i++)
{
Assert.IsFalse(target.HasReached(ga));
}
}
public void HasReached_GenerationNumberGreaterOrEqualThanCondition_True()
{
var repository = new MockRepository();
var ga = repository.StrictMock <IGeneticAlgorithm> ();
using (repository.Ordered()) {
ga.Expect(g => g.GenerationsNumber).Return(10);
ga.Expect(g => g.GenerationsNumber).Return(11);
}
repository.ReplayAll();
var target = new GenerationNumberTermination(10);
Assert.IsTrue(target.HasReached(ga));
Assert.IsTrue(target.HasReached(ga));
}
public void Start_ParallelManyGenerations_Faster()
{
var selection = new EliteSelection();
var crossover = new OnePointCrossover(2);
var mutation = new UniformMutation();
var chromosome = new ChromosomeStub();
var fitness = new FitnessStub()
{
SupportsParallel = true, ParallelSleep = 1
};
var generationStrategy = new TrackingGenerationStrategy();
var termination = new GenerationNumberTermination(100);
// we test linear first
var target = new GeneticAlgorithm(new Population(10, 10, chromosome), fitness
, selection, crossover, mutation);
target.Population.GenerationStrategy = generationStrategy;
target.Termination = termination;
target.TaskExecutor = new LinearTaskExecutor();
target.Start();
Assert.AreEqual(100, target.Population.Generations.Count);
var linearTime = target.TimeEvolving.TotalMilliseconds;
//then parallel
target = new GeneticAlgorithm(new Population(10, 10, chromosome), fitness
, selection, crossover, mutation);
target.Population.GenerationStrategy = generationStrategy;
target.Termination = termination;
target.TaskExecutor = new TplTaskExecutor();
target.Start();
Assert.AreEqual(100, target.Population.Generations.Count);
var parallelTime = target.TimeEvolving.TotalMilliseconds;
Assert.Less(parallelTime, linearTime);
}
private static IAlgoritmo CriaAlgoritmoGenetico(Dictionary <string, string[]> dict, List <string> flat, Problema problema)
{
int populacaoMin, populacaoMax;
IPopulation population;
ISelection selection;
ICrossover crossover;
IMutation mutation;
ITermination termination;
IReinsertion reinsertion;
float crossoverProbability, mutationProbability;
var p = dict.ValueOrDefault("p", "50,100").Split(new[] { ',' });
if (p.Length != 2 || !int.TryParse(p[0], out populacaoMin) || !int.TryParse(p[1], out populacaoMax))
{
throw new ArgumentException("Faixa de população inválida.");
}
population = new Population(populacaoMin, populacaoMax, new CromossomoViajante(problema.Mapa.Locais.Count));
switch (dict.ValueOrDefault("s", "t"))
{
case "e":
selection = new EliteSelection();
break;
case "r":
selection = new RouletteWheelSelection();
break;
case "s":
selection = new StochasticUniversalSamplingSelection();
break;
case "t":
selection = new TournamentSelection();
break;
default:
throw new ArgumentException("Seleção inválida.");
}
switch (dict.ValueOrDefault("c", "o"))
{
case "s":
crossover = new CutAndSpliceCrossover();
break;
case "c":
crossover = new CycleCrossover();
break;
case "o":
crossover = new OrderedCrossover();
break;
case "ob":
crossover = new OrderBasedCrossover();
break;
case "op":
crossover = new OnePointCrossover();
break;
case "pm":
crossover = new PartiallyMappedCrossover();
break;
case "p":
crossover = new PositionBasedCrossover();
break;
case "tpa":
crossover = new ThreeParentCrossover();
break;
case "tp":
crossover = new TwoPointCrossover();
break;
case "u":
crossover = new UniformCrossover();
break;
default:
throw new ArgumentException("Crossover inválido.");
}
switch (dict.ValueOrDefault("m", "r"))
{
case "d":
mutation = new DisplacementMutation();
break;
case "f":
mutation = new FlipBitMutation();
break;
case "i":
mutation = new InsertionMutation();
break;
case "s":
mutation = new PartialShuffleMutation();
break;
case "r":
mutation = new ReverseSequenceMutation();
break;
case "t":
mutation = new TworsMutation();
break;
case "u":
mutation = new UniformMutation();
break;
default:
throw new ArgumentException("Mutação inválida.");
}
switch (dict.ValueOrDefault("t", "s"))
{
case "s":
termination = new FitnessStagnationTermination();
break;
case "t":
termination = new FitnessThresholdTermination();
break;
case "g":
termination = new GenerationNumberTermination();
break;
default:
throw new ArgumentException("Terminação inválida.");
}
switch (dict.ValueOrDefault("e", "e"))
{
case "e":
reinsertion = new ElitistReinsertion();
break;
case "p":
reinsertion = new PureReinsertion();
break;
case "u":
reinsertion = new UniformReinsertion();
break;
default:
throw new ArgumentException("Reinserção inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("cp", "0,75"), out crossoverProbability))
{
throw new ArgumentException("Probabilidade de crossover inválida.");
}
if (!float.TryParse(dict.ValueOrDefault("mp", "0,25"), out mutationProbability))
{
throw new ArgumentException("Probabilidade de mutação inválida.");
}
return(new AlgoritmoGenetico(problema, population, selection, crossover, crossoverProbability, mutation, mutationProbability, termination, reinsertion));
}
/// <summary>
/// Initializes a new instance of the <see cref="AlgorithmOptimumFinder"/> class
/// </summary>
/// <param name="start">Algorithm start date</param>
/// <param name="end">Algorithm end date</param>
/// <param name="fitScore">Argument of <see cref="FitnessScore"/> type. Fintess function to rank the backtest results</param>
/// <param name="filterEnabled">Indicates whether to apply fitness filter to backtest results</param>
public AlgorithmOptimumFinder(DateTime start, DateTime end, FitnessScore fitScore, bool filterEnabled)
{
// Assign Dates and Criteria to sort the results
StartDate = start;
EndDate = end;
FitnessScore = fitScore;
// Common properties
var selection = new RouletteWheelSelection();
// Properties specific to optimization modes
IFitness fitness;
PopulationBase population;
ITaskExecutor executor;
ITermination termination;
// Task execution mode
switch (Shared.Config.TaskExecutionMode)
{
// Enable fitness filtering while searching for optimum parameters
case TaskExecutionMode.Linear:
executor = new LinearTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Parallel:
executor = new ParallelTaskExecutor();
fitness = new OptimizerFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
case TaskExecutionMode.Azure:
executor = new ParallelTaskExecutor();
fitness = new AzureFitness(StartDate, EndDate, fitScore, filterEnabled);
break;
default:
throw new Exception("Executor initialization failed");
}
// Optimization mode
switch (Shared.Config.OptimizationMode)
{
case OptimizationMode.BruteForce:
{
// Create cartesian population
population = new PopulationCartesian(Shared.Config.GeneConfigArray);
termination = new GenerationNumberTermination(1);
break;
}
case OptimizationMode.Genetic:
{
// Create random population
population = new PopulationRandom(Shared.Config.GeneConfigArray, Shared.Config.PopulationInitialSize)
{
GenerationMaxSize = Shared.Config.GenerationMaxSize
};
// Logical terminaton
var localTerm = new LogicalOrTermination();
localTerm.AddTermination(new FruitlessGenerationsTermination(3));
if (Shared.Config.Generations.HasValue)
{
localTerm.AddTermination(new GenerationNumberTermination(Shared.Config.Generations.Value));
}
if (Shared.Config.StagnationGenerations.HasValue)
{
localTerm.AddTermination(new FitnessStagnationTermination(Shared.Config.StagnationGenerations.Value));
}
termination = localTerm;
break;
}
default:
throw new Exception("Optimization mode specific objects were not initialized");
}
// Create GA itself
GenAlgorithm = new GeneticAlgorithm(population, fitness, executor)
{
// Reference types
Selection = selection,
Termination = termination,
// Values types
CrossoverParentsNumber = Shared.Config.CrossoverParentsNumber,
CrossoverMixProbability = Shared.Config.CrossoverMixProbability,
MutationProbability = Shared.Config.MutationProbability
};
}
public static void BuildTestGA()
{
//Create chromosome length = 20
BinaryChromosome chrom = new BinaryChromosome(20);
//Create population = [2,100]
var population = new Population(2, 100, chrom);
//Console.WriteLine(chrom.Length);
//Console.WriteLine(chrom.ToString());
//create Fitness Function (функция приспособления)
var fitness = new FuncFitness(
(c) =>
{
var fc = c as BinaryChromosome;
double result = 0.0;
foreach (Gene gene in fc.GetGenes())
{
result += Convert.ToDouble(gene.Value.ToString());
}
return(result);
}
);
//create selection
//var selection = new EliteSelection();
var selection = new TournamentSelection();
//var selection = new RouletteWheelSelection();
//var selection = new StochasticUniversalSamplingSelection();
//create crossover
var crossover = new UniformCrossover(0.5f);
//var crossover = new CutAndSpliceCrossover(); //только с EliteSelection()
//var crossover = new OnePointCrossover();
//var crossover = new TwoPointCrossover();
//var crossover = new CycleCrossover(); // new OrderBasedCrossover(); new OrderedCrossover(); new PositionBasedCrossover(); new PartiallyMappedCrossover(); //может использоваться только с упорядоченными хромосомами. Указанная хромосома имеет повторяющиеся гены
//var crossover = new ThreeParentCrossover(); //ОДНУ Итерацию выполняет
//create mutation
var mutation = new FlipBitMutation();
//var mutation = new UniformMutation(); //1 перегрузка принимает индексы генов для мутации, 2-я все гены мутируют
//var mutation = new TworsMutation(); //Слабая
//var mutation = new ReverseSequenceMutation(); //Слабая
//create termination (Количество итераций)
var termination = new GenerationNumberTermination(100);
//var termination = new FitnessStagnationTermination(50);
// var termination = new FitnessThresholdTermination(50); //Постоянно зацикливается
//TimeSpan time = new TimeSpan(0, 0, 10); //10 секунд
//var termination = new TimeEvolvingTermination(time);
//Сам генетический алгоритм
var ga = new GeneticAlgorithm(
population,
fitness,
selection,
crossover,
mutation);
ga.Termination = termination;
Console.WriteLine("Generation: = distance");
var latestFitness = 0.0;
ga.GenerationRan += (sender, e) =>
{
var bestChromosome = ga.BestChromosome as BinaryChromosome;
var bestFitness = bestChromosome.Fitness.Value;
if (bestFitness != latestFitness)
{
latestFitness = bestFitness;
var phenotype = bestChromosome.GetGenes();
//Console.WriteLine(
// "Generation {0,2}: ({1},{2}),({3},{4}) = {5}",
// ga.GenerationsNumber,
// phenotype[0],
// phenotype[1],
// phenotype[2],
// phenotype[3],
// bestFitness
//);
Console.WriteLine("Generation {0,2}. Best Fitness = {1}", ga.GenerationsNumber, bestFitness);
Console.Write("Chromosome: ");
foreach (Gene g in phenotype)
{
Console.Write(g.Value.ToString() + "");
}
Console.WriteLine();
}
};
ga.Start();
}
private void btnStart_Click(object sender, EventArgs e)
{
Models.Instance i = new Models.Instance();
i.Days = 7;
i.Doctors = doctors;
int min = (int)numMin.Value;
int max = (int)numMax.Value;
var chromosome = new Models.Chromosome(21, i, r);
var population = new Population(min, max, chromosome);
var fitness = new Models.Fitness();
IMutation mutation = new TworsMutation();
ISelection selection = new RouletteWheelSelection();
ICrossover crossover = new OnePointCrossover(r.Next(20));
if (cbxMutation.SelectedItem.ToString() == "Insertion")
{
mutation = new InsertionMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Partial Shuffle")
{
mutation = new PartialShuffleMutation();
}
else if (cbxMutation.SelectedItem.ToString() == "Reverse Sequence")
{
mutation = new ReverseSequenceMutation();
}
if (cbxSelection.SelectedItem.ToString() == "Elitizam")
{
selection = new EliteSelection();
}
if (cbxCrossover.SelectedItem.ToString() == "Two-point")
{
int p1 = r.Next(19);
int p2 = r.Next(p1 + 1, 20);
crossover = new TwoPointCrossover(p1, p2);
}
else if (cbxCrossover.SelectedItem.ToString() == "Uniform")
{
crossover = new UniformCrossover();
}
var ga = new GeneticAlgorithm(population, fitness, selection, crossover, mutation);
ITermination termination = new FitnessStagnationTermination(50);
if (cbxTermination.SelectedItem.ToString() == "Generation number")
{
termination = new GenerationNumberTermination(200);
}
ga.Termination = termination;
ga.MutationProbability = (float)numProbability.Value;
ga.Start();
Gene[] g = ga.BestChromosome.GetGenes();
dataView.Rows.Clear();
for (int j = 0; j < 7; j++)
{
string[] row = new string[] { ((List <int>)g[j * 3].Value)[0].ToString() + " " + ((List <int>)g[j * 3].Value)[1].ToString(),
((List <int>)g[j * 3 + 1].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 1].Value)[1].ToString(),
((List <int>)g[j * 3 + 2].Value)[0].ToString() + " " + ((List <int>)g[j * 3 + 2].Value)[1].ToString() };
dataView.Rows.Add(row);
dataView.Rows[j].HeaderCell.Value = (j + 1).ToString();
}
lblFitness.Text = ga.BestChromosome.Fitness.ToString() + " , generacija broj " + ga.GenerationsNumber.ToString();
}
public void Constructor_Sets_Fields_Correctly()
{
var sot = new GenerationNumberTermination(10);
Assert.AreEqual(10, sot.MaximalNumberOfGenerations);
}
