/// <summary>
/// Represents an Entity able to execute the Genetic Algorithm for the input parameters
/// </summary>
/// <param name="problem">Instance of a Problem to be Solved</param>
/// <param name="selectionMethodType">The Method to Be used for Parent Selection in Crossover Methods</param>
/// <param name="tourSize">In case the 'Tour' Method is chosen as the current Selection Method, this parameter will be considered in its evaluation</param>
/// <param name="crossoverMethodType">The Method to be used for Crossover between two parents</param>
/// <param name="reinsertionMethodType">The Method by which Individuals are chosen to be part of the next Generation</param>
/// <param name="populationSize">The Individual Count in a Population that is considered to be a Generation</param>
/// <param name="numberOfGenerations">The Number of Generations to run before the algorithm returns an Result</param>
/// <param name="crossoverPct">The Crossover percentage involved in the calculation</param>
/// <param name="mutationPct">The Mutation percentage involved in the calculation</param>
public MonoObjectiveGeneticAlgorithm(ProblemBase problem, SelectionMethodBase selectionMethod, CrossoverMethodBase crossoverMethod, ReinsertionMethodBase reinsertionMethod, int populationSize, int numberOfGenerations, int mutationPct)
: base(problem, populationSize, numberOfGenerations)
{
this.mutationPct = mutationPct;
this.selectionMethod = selectionMethod;
selectionMethod.Problem = problem;
this.crossoverMethod = crossoverMethod;
crossoverMethod.Problem = problem;
this.reinsertionMethod = reinsertionMethod;
}
/// <summary>
/// Represents an Entity able to execute the Genetic Algorithm for the input parameters
/// </summary>
/// <param name="problem">Instance of a Problem to be Solved</param>
/// <param name="selectionMethodType">The Method to Be used for Parent Selection in Crossover Methods</param>
/// <param name="tourSize">In case the 'Tour' Method is chosen as the current Selection Method, this parameter will be considered in its evaluation</param>
/// <param name="crossoverMethodType">The Method to be used for Crossover between two parents</param>
/// <param name="reinsertionMethodType">The Method by which Individuals are chosen to be part of the next Generation</param>
/// <param name="populationSize">The Individual Count in a Population that is considered to be a Generation</param>
/// <param name="numberOfGenerations">The Number of Generations to run before the algorithm returns an Result</param>
/// <param name="crossoverPct">The Crossover percentage involved in the calculation</param>
/// <param name="mutationPct">The Mutation percentage involved in the calculation</param>
public MonoObjectiveGeneticAlgorithm(ProblemBase problem, SelectionMethodBase selectionMethod, CrossoverMethodBase crossoverMethod, ReinsertionMethodBase reinsertionMethod, int populationSize, int numberOfGenerations, int mutationPct)
: base(problem, populationSize, numberOfGenerations)
{
this.mutationPct = mutationPct;
this.selectionMethod = selectionMethod;
selectionMethod.Problem = problem;
this.crossoverMethod = crossoverMethod;
crossoverMethod.Problem = problem;
this.reinsertionMethod = reinsertionMethod;
}
private Population_MonoObjective_AG RunGeneration(Population_MonoObjective_AG currentGeneration, SelectionMethodBase selectionMethod, CrossoverMethodBase crossoverMethod, /*IReinsertionMethod reinsertionMethod, */ReinsertionMethodBase reinsertionMethod)
{
Population_MonoObjective_AG recentlyBorn = new Population_MonoObjective_AG(Problem, InitialPopulationSize);
int expectedChildCount = ((InitialPopulationSize * reinsertionMethod.OffspringPercentage) / 100);
while (recentlyBorn.IndividualCount < expectedChildCount) {
IndividualBase parent1 = null, parent2 = null, child1 = null, child2 = null;
selectionMethod.Execute(currentGeneration, out parent1, out parent2);
crossoverMethod.Execute(parent1, parent2, out child1, out child2);
recentlyBorn.AddIndividual(child1);
recentlyBorn.AddIndividual(child2);
}
int mutatedChildCount = ((recentlyBorn.IndividualCount * mutationPct) / 100);
for (int idx = 0; idx < mutatedChildCount; ++idx) {
IndividualBase randomIndividual = recentlyBorn.GetRandomIndividual();
Problem.MutateIndividual(randomIndividual);
}
IndividualBase individual = null;
for (int idx = 0; idx < recentlyBorn.IndividualCount; ++idx) {
individual = recentlyBorn.Content[idx];
Problem.ValidateIndividual(individual);
IndividualEvaluator.Execute(individual, Problem);
}
Population_MonoObjective_AG newGeneration = reinsertionMethod.Execute(currentGeneration, recentlyBorn);
return newGeneration;
}
private Population_MonoObjective_AG RunGeneration(Population_MonoObjective_AG currentGeneration, SelectionMethodBase selectionMethod, CrossoverMethodBase crossoverMethod, /*IReinsertionMethod reinsertionMethod, */ ReinsertionMethodBase reinsertionMethod)
{
Population_MonoObjective_AG recentlyBorn = new Population_MonoObjective_AG(Problem, InitialPopulationSize);
int expectedChildCount = ((InitialPopulationSize * reinsertionMethod.OffspringPercentage) / 100);
while (recentlyBorn.IndividualCount < expectedChildCount)
{
IndividualBase parent1 = null, parent2 = null, child1 = null, child2 = null;
selectionMethod.Execute(currentGeneration, out parent1, out parent2);
crossoverMethod.Execute(parent1, parent2, out child1, out child2);
recentlyBorn.AddIndividual(child1);
recentlyBorn.AddIndividual(child2);
}
int mutatedChildCount = ((recentlyBorn.IndividualCount * mutationPct) / 100);
for (int idx = 0; idx < mutatedChildCount; ++idx)
{
IndividualBase randomIndividual = recentlyBorn.GetRandomIndividual();
Problem.MutateIndividual(randomIndividual);
}
IndividualBase individual = null;
for (int idx = 0; idx < recentlyBorn.IndividualCount; ++idx)
{
individual = recentlyBorn.Content[idx];
Problem.ValidateIndividual(individual);
IndividualEvaluator.Execute(individual, Problem);
}
Population_MonoObjective_AG newGeneration = reinsertionMethod.Execute(currentGeneration, recentlyBorn);
return(newGeneration);
}