public TIndividual Run(XFitnessFunction <TChromosome> fitnessFunction)
{
int generation = 0;
var reproductionGroup = new List <TIndividual>();
var currentPopulationsChromosomes = PopulationInitializer.Initialize();
IReadOnlyList <TIndividual> currentPopulation = currentPopulationsChromosomes.Select(
chromosome => IndividualFactory.CreateIndividual(chromosome, fitnessFunction)
).ToList();
var bestSolution = currentPopulation[0];
foreach (var individual in currentPopulation)
{
if (individual.Fitness > bestSolution.Fitness)
{
bestSolution = individual;
}
}
while (ContinueCondition.ShouldContinue(currentPopulation, generation))
{
var pairs = Breeding.Select(currentPopulation);
reproductionGroup.Clear();
foreach (var Compound in pairs.Select(pair => Crossover.Crossover(pair)))
{
reproductionGroup.AddRange(
Compound
.Select(chromosome => Mutation.Mutate(chromosome))
.Select(mutant => IndividualFactory.CreateIndividual(mutant, fitnessFunction)));
}
foreach (var individual in reproductionGroup)
{
if (individual.Fitness > bestSolution.Fitness)
{
bestSolution = individual;
}
}
currentPopulation = Strategy.NextGeneration(
currentPopulation,
reproductionGroup
);
generation++;
}
return(bestSolution);
}
public IMeal Solve(IEnumerable <DietaryReferenceIntake> maxWeight, IEnumerable <Food> items, int iterationCount)
{
MaxWeight = maxWeight;
Items = items;
IterationCount = iterationCount;
var population = PopulationInitializer.InitializePopulation(MaxWeight, items);
for (int i = 0; i < IterationCount; i++)
{
if (IsDebug)
{
Console.WriteLine($"{population.GetFittest().TotalCostSum:F2}");
}
population.RemoveNotFit();
PopulationCrossoverer.CrossoverPopulation(population);
PopulationMutator.MutatePopulation(population);
population.MergeNewGeneration();
}
return(population.GetFittest());
}
