public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network, bool reset, double[][] input, double[][] ideal,
int populationSize, double mutationPercent, double allowedPercentageToMate)
{
MutationPercent = mutationPercent;
MatingPopulation = allowedPercentageToMate * 2;
PopulationSize = populationSize;
PercentToMate = allowedPercentageToMate;
Input = input;
Ideal = ideal;
Chromosomes = new TrainingSetNeuralChromosome[PopulationSize];
for (var i = 0; i < Chromosomes.Length; i++)
{
var chromosomeNetwork = (FeedforwardNetwork)network.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
var c = new TrainingSetNeuralChromosome(this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network, bool reset, double[][] input, double[][] ideal,
int populationSize, double mutationPercent, double allowedPercentageToMate)
{
MutationPercent = mutationPercent;
MatingPopulation = allowedPercentageToMate * 2;
PopulationSize = populationSize;
PercentToMate = allowedPercentageToMate;
Input = input;
Ideal = ideal;
Chromosomes = new TrainingSetNeuralChromosome[PopulationSize];
for (var i = 0; i < Chromosomes.Length; i++)
{
var chromosomeNetwork = (FeedforwardNetwork)network.Clone();
if (reset)
chromosomeNetwork.Reset();
var c = new TrainingSetNeuralChromosome(this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
