public GA(IDataSet <T>[] ds, IBreeder <T> _breeder, double _mutationRate)
{
data = ds;
breeder = _breeder;
mutationRate = _mutationRate;
myFactory.Factory.init();
r = new Random(DateTime.Now.Millisecond);
}
static void Main(string[] args)
{
NeuralNetworkConfigurationSettings networkConfig = new NeuralNetworkConfigurationSettings
{
NumInputNeurons = 1,
NumOutputNeurons = 1,
NumHiddenLayers = 2,
NumHiddenNeurons = 3,
SummationFunction = new SimpleSummation(),
ActivationFunction = new TanhActivationFunction()
};
GenerationConfigurationSettings generationSettings = new GenerationConfigurationSettings
{
UseMultithreading = true,
GenerationPopulation = 500
};
EvolutionConfigurationSettings evolutionSettings = new EvolutionConfigurationSettings
{
NormalMutationRate = 0.05,
HighMutationRate = 0.5,
GenerationsPerEpoch = 10,
NumEpochs = 1000,
NumTopEvalsToReport = 10
};
MutationConfigurationSettings mutationSettings = new MutationConfigurationSettings
{
MutateAxonActivationFunction = true,
MutateNumberOfHiddenLayers = true,
MutateNumberOfHiddenNeuronsInLayer = true,
MutateSomaBiasFunction = true,
MutateSomaSummationFunction = true,
MutateSynapseWeights = true
};
var random = new RandomWeightInitializer(new Random());
INeuralNetworkFactory factory = NeuralNetworkFactory.GetInstance(SomaFactory.GetInstance(networkConfig.SummationFunction), AxonFactory.GetInstance(networkConfig.ActivationFunction), SynapseFactory.GetInstance(new RandomWeightInitializer(new Random()), AxonFactory.GetInstance(networkConfig.ActivationFunction)), SynapseFactory.GetInstance(new ConstantWeightInitializer(1.0), AxonFactory.GetInstance(new IdentityActivationFunction())), random);
IBreeder breeder = BreederFactory.GetInstance(factory, random).Create();
IMutator mutator = MutatorFactory.GetInstance(factory, random).Create(mutationSettings);
IEvalWorkingSet history = EvalWorkingSetFactory.GetInstance().Create(50);
IEvaluatableFactory evaluatableFactory = new GameEvaluationFactory();
IStorageProxy proxy = new NodeJSProxy(1, "http://localhost:3000", "123456789");
IEpochAction action = new BestPerformerUpdater(proxy);
var GAFactory = GeneticAlgorithmFactory.GetInstance(evaluatableFactory);
IGeneticAlgorithm evolver = GAFactory.Create(networkConfig, generationSettings, evolutionSettings, factory, breeder, mutator, history, evaluatableFactory, action);
evolver.RunSimulation();
}
public AccidentGA(IDataSet <string[]>[] ds, IBreeder <string[]> _breeder, double mutationRate, string _filePath, IParser _parser, bool prefilleddata = false)
: base(ds, _breeder, mutationRate)
{
filePath = _filePath;
breeder = _breeder;
parser = _parser;
percMatches = new double[data.Length];
if (ds == null)
{
throw new Exception("Must initialise a dataset");
}
if (!prefilleddata)
{
GetData();
}
}
private GeneticAlgorithm(NeuralNetworkConfigurationSettings networkConfig, GenerationConfigurationSettings generationConfig, EvolutionConfigurationSettings evolutionConfig, INeuralNetworkFactory networkFactory, IBreeder breeder, IMutator mutator, IEvalWorkingSet workingSet, IEvaluatableFactory evaluatableFactory, IEpochAction epochAction)
{
_networkConfig = networkConfig;
_generationConfig = generationConfig;
_evolutionConfig = evolutionConfig;
_epochAction = epochAction;
var sessions = new List <ITrainingSession>();
_networkFactory = networkFactory;
_breeder = breeder;
_mutator = mutator;
_history = workingSet;
_evaluatableFactory = evaluatableFactory;
for (int i = 0; i < _generationConfig.GenerationPopulation; i++)
{
var network = _networkFactory.Create(_networkConfig.NumInputNeurons, _networkConfig.NumOutputNeurons, _networkConfig.NumHiddenLayers, _networkConfig.NumHiddenNeurons);
sessions.Add(new TrainingSession(network, _evaluatableFactory.Create(network), i));
}
_generation = new Generation(sessions, _generationConfig);
}
public Generator(int population, int cityCount, int generations, double mutationRate, int seed)
{
this.logger = new GeneratorLogger();
this.population = population;
this.cityCount = cityCount;
this.generations = generations;
this.mutationRate = mutationRate;
this.seed = seed;
this.abstractFactory = new AbstractFactory();
this.cityFactory = abstractFactory.CreateCityFactory(this.seed);
this.breedingFactory = abstractFactory.CreateBreedingFactory();
ISolutionFactory solutionFactory = abstractFactory.CreateSolutionFactory();
IMutater mutater = breedingFactory.CreateMutater(mutationRate);
this.solutionFactory = solutionFactory;
this.bestFit = new BestFit();
this.breeder = breedingFactory.CreateBreeder(mutater, solutionFactory);
}
public IGeneticAlgorithm Create(NeuralNetworkConfigurationSettings networkConfig, GenerationConfigurationSettings generationConfig, EvolutionConfigurationSettings evolutionConfig, INeuralNetworkFactory networkFactory, IBreeder breeder, IMutator mutator, IEvalWorkingSet workingSet, IEvaluatableFactory evaluatableFactory, IEpochAction epochAction)
{
return(GeneticAlgorithm.GetInstance(networkConfig, generationConfig, evolutionConfig, _networkFactory, breeder, mutator, workingSet, _evaluatableFactory, epochAction));
}
public IGeneticAlgorithm Create(NeuralNetworkConfigurationSettings networkConfig, GenerationConfigurationSettings generationConfig, EvolutionConfigurationSettings evolutionConfig, INeuralNetworkFactory networkFactory, IBreeder breeder, IMutator mutator, IEvalWorkingSet workingSet, IEvaluatableFactory evaluatableFactory, IEpochAction epochAction)
{
return GeneticAlgorithm.GetInstance(networkConfig, generationConfig, evolutionConfig, _networkFactory, breeder, mutator, workingSet, _evaluatableFactory, epochAction);
}
public void Solve(BionicModel model, BionicSolverSettings settings)
{
if (model == null)
{
throw new ArgumentNullException("model");
}
if (settings == null)
{
throw new ArgumentNullException("settings");
}
OnSolverStarting(new EventArgs());
IInitialPopulationGenerator initializer = InitialPopulationGeneratorFactory.CreateInitialPopulationGenerator();
ISelector selector = SelectorFactory.CreateSelector();
IBreeder breeder = BreederFactory.CreateBreeder();
// 1. Zeroing generation number
model.CurrentGeneration = 0;
OnSolverStarted(new EventArgs());
// 2. Creating initial population
CopyIndividuals(initializer.GenerateInitialPopulation(settings.InitialPopulationSize, model.Attributes), model.CurrentPopulation);
CalculateFitness(model, model.CurrentPopulation, settings);
model.ApplyFunctionalConstraints();
OnPopulationUpdated(new EventArgs());
while (model.CurrentGeneration < settings.MaxGenerations)
{
// 3. Selecting most fit individuals
IEnumerable <Individual> fitIndividuals = selector.Select(model.CurrentPopulation, model.FitnessCriterion, settings.SelectionCap);
if (fitIndividuals.Count() == 0)
{
OnSolverFinished(new SolverFinishedEventArgs("All individuals are inactive"));
return;
}
// Selected subpopulation will contain only selected active individuals
Population selectedSubPopulation = new Population(fitIndividuals.Count());
foreach (Individual fitIndividual in fitIndividuals)
{
selectedSubPopulation.Add(fitIndividual);
}
// 4. Breeding the selected ones
IEnumerable <Individual> descendants = breeder.Breed(selectedSubPopulation, model.Attributes,
settings.DescendantsRangePercent, settings.DescendantsNum, model.CurrentGeneration + 1);
if (descendants.Count() == 0)
{
OnSolverFinished(new SolverFinishedEventArgs("No descendants was created"));
return;
}
// Descendants subpopulation will contain only new individuals
Population descendantsSubPopulation = new Population(descendants.Count());
foreach (Individual descendant in descendants)
{
descendantsSubPopulation.Add(descendant);
}
// 5. Calculating fitness of the descendants
CalculateFitness(model, descendantsSubPopulation, settings);
// At this point, all descendants are still active, although
// there may be individuals that don't fit f. c.
// 6. Forming new population
if (settings.ApplyElitism)
{
// First unite descendants with their parents (elitism)
// We can do this without fear of ID collision because this
// was taken into account on the breeding step
CopyIndividuals(descendantsSubPopulation, selectedSubPopulation);
// Now this united population becomes current, we just
// throw away old individuals
model.CurrentPopulation.Clear();
CopyIndividuals(selectedSubPopulation, model.CurrentPopulation);
}
else
{
// We throw away parents and old individuals,
// descendants are taking over the place
model.CurrentPopulation.Clear();
CopyIndividuals(descendantsSubPopulation, model.CurrentPopulation);
}
model.ApplyFunctionalConstraints();
// 7. Updating current generation number
model.CurrentGeneration = model.CurrentPopulation.Max(ind => ind.Value.GenerationNumber);
OnPopulationUpdated(new EventArgs());
}
OnSolverFinished(new SolverFinishedEventArgs());
}
public FloatGA(IDataSet <float>[] ds, IBreeder <float> _breeder, double mutationRate)
: base(ds, _breeder, mutationRate)
{
breeder = _breeder;
}
