/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
///
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer, ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate) : base(TrainingImplementationType.Iterative)
{
Genetic = new NeuralGeneticAlgorithmHelper
{
CalculateScore = new GeneticScoreAdapter(calculateScore)
};
IPopulation population = new BasicPopulation(populationSize);
Genetic.MutationPercent = mutationPercent;
Genetic.MatingPopulation = percentToMate*2;
Genetic.PercentToMate = percentToMate;
Genetic.Crossover = new Splice(network.Structure.CalculateSize()/3);
Genetic.Mutate = new MutatePerturb(4.0d);
Genetic.Population = population;
for (int i = 0; i < population.PopulationSize; i++)
{
var chromosomeNetwork = (BasicNetwork) (network
.Clone());
randomizer.Randomize(chromosomeNetwork);
var genome = new NeuralGenome(chromosomeNetwork) {GA = Genetic};
Genetic.PerformCalculateScore(genome);
Genetic.Population.Add(genome);
}
population.Sort();
}
/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
///
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer, ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate) : base(TrainingImplementationType.Iterative)
{
Genetic = new NeuralGeneticAlgorithmHelper
{
CalculateScore = new GeneticScoreAdapter(calculateScore)
};
IPopulation population = new BasicPopulation(populationSize);
Genetic.MutationPercent = mutationPercent;
Genetic.MatingPopulation = percentToMate * 2;
Genetic.PercentToMate = percentToMate;
Genetic.Crossover = new Splice(network.Structure.CalculateSize() / 3);
Genetic.Mutate = new MutatePerturb(4.0d);
Genetic.Population = population;
for (int i = 0; i < population.PopulationSize; i++)
{
var chromosomeNetwork = (BasicNetwork)(network
.Clone());
randomizer.Randomize(chromosomeNetwork);
var genome = new NeuralGenome(chromosomeNetwork)
{
GA = Genetic
};
Genetic.PerformCalculateScore(genome);
Genetic.Population.Add(genome);
}
population.Sort();
}
/// <summary>
/// Construct a neural genetic algorithm.
/// </summary>
/// <param name="network">The network to base this on.</param>
/// <param name="randomizer">The randomizer used to create this initial population.</param>
/// <param name="calculateScore">The score calculation object.</param>
/// <param name="populationSize">The population size.</param>
/// <param name="mutationPercent">The percent of offspring to mutate.</param>
/// <param name="percentToMate">The percent of the population allowed to mate.</param>
public NeuralGeneticAlgorithm(BasicNetwork network,
IRandomizer randomizer,
ICalculateScore calculateScore,
int populationSize, double mutationPercent,
double percentToMate)
{
this.Helper = new NeuralGeneticAlgorithmHelper();
this.Helper.CalculateScore = new GeneticScoreAdapter(calculateScore);
IPopulation population = new BasicPopulation(populationSize);
Helper.MutationPercent = mutationPercent;
Helper.MatingPopulation = (percentToMate * 2);
Helper.PercentToMate = (percentToMate);
Helper.Crossover = (new Splice(network.Structure.CalculateSize() / 3));
Helper.Mutate = (new MutatePerturb(4.0));
Helper.Population = (population);
for (int i = 0; i < population.PopulationSize; i++)
{
BasicNetwork chromosomeNetwork = (BasicNetwork)network
.Clone();
randomizer.Randomize(chromosomeNetwork);
NeuralGenome genome =
new NeuralGenome(this, chromosomeNetwork);
Helper.PerformScoreCalculation(genome);
Helper.Population.Add(genome);
}
population.Sort();
}
public NeuralGeneticAlgorithm(BasicNetwork network, IRandomizer randomizer, ICalculateScore calculateScore, int populationSize, double mutationPercent, double percentToMate)
: base(TrainingImplementationType.Iterative)
{
NeuralGeneticAlgorithmHelper helper;
Label_012F:
helper = new NeuralGeneticAlgorithmHelper();
helper.CalculateScore = new GeneticScoreAdapter(calculateScore);
this.Genetic = helper;
IPopulation population = new BasicPopulation(populationSize);
this.Genetic.MutationPercent = mutationPercent;
this.Genetic.MatingPopulation = percentToMate * 2.0;
this.Genetic.PercentToMate = percentToMate;
this.Genetic.Crossover = new Splice(network.Structure.CalculateSize() / 3);
this.Genetic.Mutate = new MutatePerturb(4.0);
this.Genetic.Population = population;
int num = 0;
while (true)
{
NeuralGenome genome2;
if (num < population.PopulationSize)
{
BasicNetwork network2 = (BasicNetwork) network.Clone();
randomizer.Randomize(network2);
if ((((uint) percentToMate) + ((uint) populationSize)) >= 0)
{
genome2 = new NeuralGenome(network2) {
GA = this.Genetic
};
}
}
else
{
population.Sort();
if (((uint) populationSize) <= uint.MaxValue)
{
return;
}
goto Label_012F;
}
if ((((uint) num) + ((uint) populationSize)) >= 0)
{
NeuralGenome g = genome2;
this.Genetic.PerformCalculateScore(g);
if (((uint) populationSize) > uint.MaxValue)
{
goto Label_012F;
}
this.Genetic.Population.Add(g);
num++;
}
}
}