public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
if (count <= 0)
{
throw new ArgumentException($"{nameof(count)} must be greater than 0. Actual: {count}.");
}
if (generator == null)
{
throw new System.ArgumentNullException(nameof(generator));
}
if (evaluator == null)
{
throw new System.ArgumentNullException(nameof(evaluator));
}
TargetSize = count;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var fitness = evaluator.ComputeFitness(value);
var chromosome = new Chromosome <T>(value, fitness);
Chromosomes.Add(chromosome);
}
}
Chromosome <string> ICrossOver <string> .CrossOver(Chromosome <string> parent1, Chromosome <string> parent2)
{
var parent1Genes = parent1.Genes;
var parent2Genes = parent2.Genes;
var index = Random.Next(0, parent1.Genes.Length);
var newChars = new char[parent1Genes.Length];
for (int i = 0; i < newChars.Length; i++)
{
newChars[i] = i >= index ? parent2Genes[i] : parent1Genes[i];
}
var chromosome = new Chromosome <string>(new string(newChars));
chromosome.Fitness = Evaluator.ComputeFitness(chromosome.Genes);
return(chromosome);
}
public Population(int count, IPopulationGenerator <T> generator, IFitnessEvaluator <T> evaluator)
{
Contract.Requires <ArgumentOutOfRangeException>(count > 0);
Contract.Requires <ArgumentNullException>(generator != null);
Contract.Requires <ArgumentNullException>(evaluator != null);
TargetSize = count;
Evaluator = evaluator;
// TODO: This really shouldn't be here. We should instead pass the
// population
for (var i = 0; i < count; i++)
{
var value = generator.Generate();
var chromosome = new Chromosome <T>(value);
chromosome.Fitness = evaluator.ComputeFitness(chromosome.Genes);
Chromosomes.Add(chromosome);
}
}
private Chromosome <string> Mutate(Chromosome <string> chromosome)
{
var genes = chromosome.Genes;
var random = new Random();
var index = random.Next(0, genes.Length - 1);
var min = 97;
var max = 122;
var newCharacter = (char)random.Next(min, max);
var chars = genes.ToCharArray();
chars[index] = newCharacter;
var newGenes = new string(chars);
return(new Chromosome <string>(new string(chars), Evaluator.ComputeFitness(newGenes)));
}
