public MinCog(int populationSize, int generations, double mutationRate,
double crossoverRate, string selectionProtocol, string selectionMechanism)
{
GeneticOperators = new BinaryOperators();
PopulationSize = populationSize;
Generations = generations;
MutationRate = mutationRate;
CrossoverRate = crossoverRate;
switch (selectionMechanism.ToLower())
{
case "fitness-prop":
ParentSelector = new FitnessProportionate();
SelectionMechanism = selectionMechanism;
break;
case "sigma":
ParentSelector = new SigmaScaling();
SelectionMechanism = selectionMechanism;
break;
case "tournament":
ParentSelector = new Tournament();
SelectionMechanism = selectionMechanism;
break;
case "rank":
ParentSelector = new Rank();
SelectionMechanism = selectionMechanism;
break;
}
var translator = new MinCogTranslator();
FitnessEvaluator = new MinCogFitness();
Population = new BinaryPopulation(PopulationSize, 272, selectionProtocol, FitnessEvaluator, translator, 0, 2);
FitnessEvaluator.CalculatePopulationFitness(Population.CurrentPopulation);
}
public OneMax(int populationSize, int generations, List<int> goalVector,
double mutationRate, double crossoverRate, string selectionProtocol, string selectionMechanism)
{
FitnessEvaluator = new OneMaxFitness(goalVector);
GeneticOperators = new BinaryOperators();
PopulationSize = populationSize;
Generations = generations;
MutationRate = mutationRate;
CrossoverRate = crossoverRate;
switch (selectionMechanism.ToLower())
{
case "fitness-prop":
ParentSelector = new FitnessProportionate();
SelectionMechanism = selectionMechanism;
break;
case "sigma":
ParentSelector = new SigmaScaling();
SelectionMechanism = selectionMechanism;
break;
case "tournament":
ParentSelector = new Tournament();
SelectionMechanism = selectionMechanism;
break;
case "rank":
ParentSelector = new Rank();
SelectionMechanism = selectionMechanism;
break;
}
Population = new BinaryPopulation(PopulationSize, goalVector.Count, selectionProtocol, FitnessEvaluator,
new OneMaxTranslator(), 0, 2);
FitnessEvaluator.CalculatePopulationFitness(Population.CurrentPopulation);
}
public SpikingNeuron(int populationSize, int generations, int dataSetNumber,
double mutationRate, double crossoverRate, string selectionProtocol,
string selectionMechanism, string sdm)
{
GeneticOperators = new BinaryOperators();
PopulationSize = populationSize;
Generations = generations;
MutationRate = mutationRate;
CrossoverRate = crossoverRate;
K = 0;
J = 0;
PrevFitness = 0.0;
switch (selectionMechanism.ToLower())
{
case "fitness-prop":
ParentSelector = new FitnessProportionate();
SelectionMechanism = selectionMechanism;
break;
case "sigma":
ParentSelector = new SigmaScaling();
SelectionMechanism = selectionMechanism;
break;
case "tournament":
ParentSelector = new Tournament();
SelectionMechanism = selectionMechanism;
break;
case "rank":
ParentSelector = new Rank();
SelectionMechanism = selectionMechanism;
break;
}
GoalSpike = readDataSet(dataSetNumber);
FitnessEvaluator = new IzhikevichFitness(sdm, GoalSpike);
Population = new BinaryPopulation(PopulationSize, 35, selectionProtocol, FitnessEvaluator,
new IzhikevichTranslator(), 0, 2);
FitnessEvaluator.CalculatePopulationFitness(Population.CurrentPopulation);
}
public ColonelBlotto(int populationSize, int generations, int numberOfBattles,
double mutationRate, double crossoverRate, string selectionProtocol,
string selectionMechanism, double redeploymentFraction, double lossFraction)
{
FitnessEvaluator = new ColonelBlottoFitness();
GeneticOperators = new BinaryOperators();
PopulationSize = populationSize;
Generations = generations;
MutationRate = mutationRate;
CrossoverRate = crossoverRate;
NumberOfBattles = numberOfBattles;
RedeploymentFraction = redeploymentFraction;
LossFraction = lossFraction;
AverageEntropy = 0.0;
switch (selectionMechanism.ToLower())
{
case "fitness-prop":
ParentSelector = new FitnessProportionate();
SelectionMechanism = selectionMechanism;
break;
case "sigma":
ParentSelector = new SigmaScaling();
SelectionMechanism = selectionMechanism;
break;
case "tournament":
ParentSelector = new Tournament();
SelectionMechanism = selectionMechanism;
break;
case "rank":
ParentSelector = new Rank();
SelectionMechanism = selectionMechanism;
break;
}
Population = new BinaryPopulation(PopulationSize, NumberOfBattles, selectionProtocol, FitnessEvaluator,
new ColonelBlottoTranslator(), 0, 11);
}