/// <summary>
/// Construct a genetic algorithm for a neural network that uses training sets.
/// </summary>
/// <param name="network">The neural network.</param>
/// <param name="reset">Should each neural network be reset to random values.</param>
/// <param name="input">The input training set.</param>
/// <param name="ideal">The ideal values for the input training set.</param>
/// <param name="populationSize">The initial population size.</param>
/// <param name="mutationPercent">The mutation percentage.</param>
/// <param name="percentToMate">The percentage of the population allowed to mate.</param>
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network,
bool reset, double[][] input,
double[][] ideal, int populationSize,
double mutationPercent, double percentToMate)
{
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.input = input;
this.ideal = ideal;
this.Chromosomes = new TrainingSetNeuralChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TrainingSetNeuralChromosome c = new TrainingSetNeuralChromosome(
this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}
/// <summary>
/// Construct a genetic algorithm for a neural network that uses training sets.
/// </summary>
/// <param name="network">The neural network.</param>
/// <param name="reset">Should each neural network be reset to random values.</param>
/// <param name="input">The input training set.</param>
/// <param name="ideal">The ideal values for the input training set.</param>
/// <param name="populationSize">The initial population size.</param>
/// <param name="mutationPercent">The mutation percentage.</param>
/// <param name="percentToMate">The percentage of the population allowed to mate.</param>
public TrainingSetNeuralGeneticAlgorithm(FeedforwardNetwork network,
bool reset, double[][] input,
double[][] ideal, int populationSize,
double mutationPercent, double percentToMate)
{
this.MutationPercent = mutationPercent;
this.MatingPopulation = percentToMate * 2;
this.PopulationSize = populationSize;
this.PercentToMate = percentToMate;
this.input = input;
this.ideal = ideal;
this.Chromosomes = new TrainingSetNeuralChromosome[this.PopulationSize];
for (int i = 0; i < this.Chromosomes.Length; i++)
{
FeedforwardNetwork chromosomeNetwork = (FeedforwardNetwork)network
.Clone();
if (reset)
{
chromosomeNetwork.Reset();
}
TrainingSetNeuralChromosome c = new TrainingSetNeuralChromosome(
this, chromosomeNetwork);
c.UpdateGenes();
SetChromosome(i, c);
}
SortChromosomes();
}