public static void RunExampleOnce()
{
List <Person> population = new List <Person>();
List <double> coefficient = new List <double>();
List <CoupleParent> coupleParents = new List <CoupleParent>();
List <string> crossoverChildren = new List <string>();
FileReader.CoefficientRead(coefficient);
FileReader.ReadFile(population, 5);
Fitness.CalculateSSE(population);
Fitness.CalculateCoefSSE(coefficient, population);
//Stap 1 t/m 5
RouletteWheel.DoCalculation(population);
//Crossover Methods
//SinglePointCrossover.DoCrossover(coupleParents, crossoverChildren);
TwoPointCrossover.DoCrossover(coupleParents, crossoverChildren, crossOverPercentage, population);
//Mutation
Mutation.MutationChildren(crossoverChildren);
//Recalculate Fitness for Childrens
List <Person> childrenPopulation = Fitness.CalculateCoefSSEChild(coefficient, crossoverChildren);
//Elitism best solution
var bestPerson = Elitism.ChildHighestFit(childrenPopulation, crossOverPercentage);
Console.ReadKey();
}
public static void RunGeneticAlgorithm()
{
for (int i = 0; i < iterations; i++)
{
List <Person> population = new List <Person>();
List <double> coefficient = new List <double>();
List <string> crossoverChildren = new List <string>();
FileReader.CoefficientRead(coefficient);
FileReader.ReadFile(population, populationAmount);
List <CoupleParent> coupleParents = new List <CoupleParent>();
Fitness.CalculateSSE(population);
Fitness.CalculateCoefSSE(coefficient, population);
RouletteWheel.DoCalculation(population);
RunCrossOver(coupleParents, crossoverChildren, population);
Mutation.MutationChildren(crossoverChildren);
//Recalculate Fitness for Childrens
List <Person> childrenPopulation = Fitness.CalculateCoefSSEChild(coefficient, crossoverChildren);
//Elitism best solution
RunElitism(i, childrenPopulation);
}
}
