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);
}
