public static OptimiserBuilder GetBuilder(DecisionSpace problemSpace)
{
var hyps = EvolutionaryAlgorithmHyperParameters.GetDefaultHyperParameters();
var population = new Population(
200);
hyps.UpdateHyperParameterValue(
EvolutionaryAlgorithmHyperParameters.Population_Size,
200);
IParentSelectionOperator parentSelector = new ParentSelectionTournament(
20, false);
IRecombinationOperator recombinationOperator = new CrossoverSimulatedBinary(
2);;
hyps.UpdateHyperParameterValue(
EvolutionaryAlgorithmHyperParameters.Number_Of_Parents, 2);
IMutationOperator mutationOperator = new MutationAddRandomNumber(
0.1,
0.1,
1);
IReinsertionOperator reinsertionOperator = new ReinsertionReplaceRandom();
return(new EvolutionaryAlgorithmBuilderContinuousMO(population, problemSpace, hyps,
parentSelector, recombinationOperator, mutationOperator, reinsertionOperator));
}
public void ReInsert_BetterThanBest_Inserts()
{
var newInd = Helpers.CreateFitnessAssessedIndividualsFromArray(
new double[][] { new[] { 1.0, 1.0 } },
new[] { testPop.Best().Fitness - 1 })
.First();
var reinserter = new ReinsertionReplaceRandom();
reinserter.ReInsert(new[] { newInd }, testPop);
Assert.Contains(newInd, testPop);
}
public void ReInsert_WorseThanWorst_DoesNotInsert()
{
var newInd = Helpers.CreateFitnessAssessedIndividualsFromArray(
new double[][] { new[] { 1.0, 1.0 } },
new[] { testPop.Worst().Fitness + 1 })
.First();
var reinserter = new ReinsertionReplaceRandom();
reinserter.ReInsert(new[] { newInd }, testPop);
Assert.DoesNotContain(newInd, testPop);
}
