public void Execute(Logger.Logger logger)
{
void logGeneration(int generation, double bestFitness, double averageFitness, double worstFitness)
{
logger.LogGeneticPeriodicTabu(generation, bestFitness, averageFitness, worstFitness, null);
}
void logOutro(List <double> bestFitnesses)
{
logger.LogOutro(bestFitnesses);
}
void logTabuCycle(int tabuCycle, double bestFitness, double currentFitness)
{
logger.LogGeneticPeriodicTabu(tabuCycle, bestFitness, null, null, currentFitness);
}
var populations = new List <List <IIndividual> >();
for (var i = 0; i < Parameters.GeneticParameters.NumAlgorithmIterations; i++)
{
populations.Add(Genetic.InitializePopulation().Select(indiv =>
{
return(Tabu.Execute(logTabuCycle, logOutro, indiv)[0]);
}).ToList());
}
Genetic.Execute(logGeneration, logOutro, Parameters.TabuParameters.NumTabuSearches, populations);
}
public void Execute(Logger.Logger logger)
{
void logGeneration(int generation, double bestFitness, double averageFitness, double worstFitness)
{
logger.LogGeneticThenSaTtp1(generation, bestFitness, averageFitness, worstFitness, null, null);
}
void logOutro(List <double> bestFitnesses)
{
logger.LogOutro(bestFitnesses);
}
var finalPopulations = Genetic.Execute(logGeneration, logOutro);
void logAnnealingCycle(int annealingCycle, double globalBestFitness, double bestFitness, double currentFitness)
{
logger.LogGeneticThenSaTtp1(annealingCycle, globalBestFitness, null, null, bestFitness, currentFitness);
}
foreach (var population in finalPopulations)
{
var bestFitness = double.MinValue;
IIndividual bestIndiv = null;
population.ForEach(indiv =>
{
var fitness = Problem.Fitness(indiv);
if (fitness > bestFitness)
{
bestFitness = fitness;
bestIndiv = indiv;
}
});
if (bestIndiv == null)
{
continue;
}
Annealing.Execute(logAnnealingCycle, logOutro, bestIndiv, Parameters.GeneticParameters.NumGenerations);
}
}
