private void Init(int numItems)
{
Started = false;
Generation = 0;
// Create the sack items
ExternalItems = new List <SackItem>();
do
{
SackItem item = new SackItem(GAUtils.RandInt(0, 100), GAUtils.RandInt(0, 10));
if (!ExternalItems.Any(i => i.Weight == item.Weight))
{
ExternalItems.Add(item);
}
} while (ExternalItems.Count < numItems);
int geneCombinations = numItems;
int chromosoneLength = geneCombinations * 1; // 1 genes per chromosone (item in or out)
// initialize the GA
Algorithm = new GeneticAlgorithm <KnapsackGenome>(
GeneticAlgorithm <KnapsackGenome> .DefaultCrossoverRate,
GeneticAlgorithm <KnapsackGenome> .DefaultMutationRate,
GeneticAlgorithm <KnapsackGenome> .DefaultPopulationSize,
chromosoneLength,
geneCombinations);
// grab the genomes
GenomePopulation = Algorithm.GrabGenomes();
}
/// <summary>
/// Takes 2 parent gene vectors, selects a midpoint and then swaps the ends
/// of each genome creating 2 new genomes which are stored in baby1 and baby2
/// </summary>
/// <param name="other"></param>
/// <param name="baby1"></param>
/// <param name="baby2"></param>
public void CrossOver(Genome <TMainModel, TCombinationModel> other, Genome <TMainModel, TCombinationModel> baby1, Genome <TMainModel, TCombinationModel> baby2)
{
// just return parents as offspring dependent on the rate
// or if parents are the same
if ((GAUtils.RandDouble() > _settings.CrossoverRate) || (this == other))
{
baby1.Genes = this.Genes;
baby2.Genes = other.Genes;
return;
}
if (baby1.Genes.Count <= 1 || baby2.Genes.Count <= 1)
{
baby1.Genes = this.Genes;
baby2.Genes = other.Genes;
return;
}
// determine a crossover point
int crossOverPoint = GAUtils.RandInt(0, this.Genes.Count - 1); // = 29
for (int i = 0; i < this.Genes.Count; i++)
{
var crossed = Genes[i].Cross(other.Genes[i], i < crossOverPoint);
baby1.Genes.Add(crossed[0]);
baby2.Genes.Add(crossed[1]);
}
}