public void GetParentPopulation_ReturnsCorrectAmountOfPotentialParents_WhenPopulationCountIsEven()
{
// Arrange
List <Solution> currentParentPopulation = SetUpCurrentParentPopulation(true);
// Act
var result = parentSelection.GetParentPopulation(currentParentPopulation);
// Assert
result.Should().HaveCount(currentParentPopulation.Count);
}
public Dictionary <int, Solution> FindSolutions()
{
parentPopulation = randomSolutionGenerator.GeneratePopulationOfRandomSolutions(GenericConstants.SIZE_OF_POPULATION);
for (int generationNum = 0; generationNum < GenericConstants.NUMBER_OF_ITERATIONS; generationNum++)
{
Random random = new Random();
List <Solution> potentialParentPool = parentSelection.GetParentPopulation(parentPopulation);
// Generate an offspring population through recombination
while (offspringPopulation.Count < parentPopulation.Count)
{
Solution parentOne = potentialParentPool.ElementAt(random.Next(0, potentialParentPool.Count - 1));
Solution parentTwo = potentialParentPool.ElementAt(random.Next(0, potentialParentPool.Count - 1));
offspringPopulation.AddRange(recombination.RecombineParents(parentOne, parentTwo));
}
// For each offspring run mutation
foreach (Solution offspring in offspringPopulation)
{
mutation.MutateSolution(offspring);
}
// Find best solution in the generation and repeat
solutions.Add(generationNum, bestSolutionFinder.GetBestSolutionInGeneration(offspringPopulation));
if (EvolutionaryAlgorithmConstants.IS_USING_ADAPTIVE_CROSSOVER_CHANCES)
{
adptiveChanceAdjuster.AdjustMutationRecombinationChances(recombination.SumOfCostOfRecombinedIndividuals, mutation.SumOfCostOfMutatedIndividuals);
ResetSumValues();
}
ResetPopulations();
}
return(solutions);
}
