public void InitGA() { System.Random rand; rand = (seedRandom) ? new System.Random(1) : new System.Random(); var randGenomGenerator = new RandNeuralGenomeGeneratorBase( rand, new Sigmoid(), weightRange, inputCount, outputCount, hiddenLayers, createBias); var reinsertion = new ReinsertBest <Synapse>( (int)(poplLne * reinsertPart)); var breeding = NewBreeding(rand); var generationGenerator = new GenerationGeneratorBase <Synapse>( poplLne, reinsertion, breeding); population = new PopulationBase <Synapse>( poplLne, randGenomGenerator, generationGenerator); }
// Create a ne random genome from the given parameters. public INeuralGenome RandomGenome( int inputs = 2, int outputs = 2, int hiddenNodes = 5, float weight = 10) { var randGenomeGenerator = new RandNeuralGenomeGeneratorBase( RandomInst, null, weight, inputs, outputs, new int[1] { hiddenNodes }, true); var result = randGenomeGenerator.NewRandomGenome(); return(result as INeuralGenome); }
public void InitGAInternal() { var randomGenomeGenerator = new RandNeuralGenomeGeneratorBase( rand, new Sigmoid(), randomWeight, inputCount, outputCount, hiddenLayers, createBias); var reinsertion = new ReinsertBest <Synapse>( (int)(poplLen * partToReinsert)); var breeding = NewBreeding(rand); var generationGenerator = new GenerationGeneratorBase <Synapse>( poplLen, reinsertion, breeding); population = new PopulationBase <Synapse>( poplLen, randomGenomeGenerator, generationGenerator); }