public List <Hypothesis> CrossoverPopulation(List <Hypothesis> population, double crossoverRate, int childPerPair)
{
var newPopulation = new List <Hypothesis>();
var popToCrossover = Math.Max(1, population.Count * crossoverRate);
var totalFitness = population.Sum(h => h.Fitness);
var minimumFitness = population.Select(h => h.Fitness).Concat(new[] { 0 }).Min();
if (minimumFitness < 0)
{
totalFitness = population.Sum(h => h.Fitness - minimumFitness);
}
for (var j = 0; j < popToCrossover; j++)
{
var candidateA = FindCrossOverCandidate(population, totalFitness, -minimumFitness);
var candidateB = FindCrossOverCandidate(population, totalFitness, -minimumFitness);
if (candidateA != null && candidateB != null)
{
for (var i = 0; i < childPerPair; i++)
{
newPopulation.Add(CrossoverMethod.Crossover(candidateA, candidateB));
}
}
}
return(newPopulation);
}
public List <Hypothesis> CrossoverPopulation(List <Hypothesis> population, double crossoverRate, int childPerPair)
{
var newPopulation = new List <Hypothesis>();
var popToCrossover = Math.Max(1, population.Count * crossoverRate);
for (var j = 0; j < popToCrossover; j++)
{
var candidateA = FindCrossOverCandidate(population);
var candidateB = FindCrossOverCandidate(population);
if (candidateA == null || candidateB == null)
{
continue;
}
for (var i = 0; i < childPerPair; i++)
{
newPopulation.Add(CrossoverMethod.Crossover(candidateA, candidateB));
}
}
return(newPopulation);
}