protected override void _crossover()
{
//clear out the population
//Cross one with the other and add the result to to pool, untill the last size of the mating pool is reached.
_population = new Population();
var numToCrossProduce = Utility.PopulationSize * Utility.MatingProportion;
var initialPopCount = _population.Count;
while (_population.Count < numToCrossProduce)
{
//Pick two random individuals
var i1 = _matingPool.GetRandomValue();
var i2 = _matingPool.GetRandomValue();
var tempChild1 = i1.Cross(i2);
var tempChild2 = i2.Cross(i1);
_population.Add(tempChild1);
_population.Add(tempChild2);
}
//keeps the best parents
_population.AddRange(_matingPool);
_population.AddRange(new Population(PerformanceTester.ValidColumnGetter(), PerformanceTester.ValidDataGetter, _selector, _flatFactory, Utility.PopulationSize - _population.Count));
_matingPool = new Population(_selector);
if (_matingPool.Count != 0)
{
throw new IndexOutOfRangeException(nameof(_matingPool) + " not zero");
}
}
/// <summary>
/// Most of the rules for a GA implementation need to be here. Most of the other parts should be relatively loosely coupled to a specific implementation or set of parameters
/// </summary>
/// <param name="db">Database which the algorithm is going to be run on</param>
/// <param name="selector"></param>
public FlatTestAlgorithm(IFitness selector) : base()
{
//Setup params for most of the class here:
_matingPool = new Population(_selector);
_selector = selector ?? throw new ArgumentNullException(nameof(selector));
_flatFactory = (validColumn, validData) => new FlatIndividual(PerformanceTester.ValidColumnGetter(), PerformanceTester.ValidDataGetter);
_population = new Population(PerformanceTester.ValidColumnGetter(), PerformanceTester.ValidDataGetter, _selector, _flatFactory);
}
