private AlgoOutput RunAlgo()
{
// Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8, 0.05, 100, 5, 9, MaxMin.Minimize);
ga.FitnessFunction = new GAFunction(theActualFunction);
//ga.FitnessFile = @"E:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values_Best;
double fitness_Best;
double[] values_Worst;
double fitness_Worst;
ga.GetBest(out values_Best, out fitness_Best);
Console.WriteLine("Best ({0}):", fitness_Best);
for (int i = 0; i < values_Best.Length; i++)
{
Console.WriteLine("{0} ", values_Best[i]);
}
ga.GetWorst(out values_Worst, out fitness_Worst);
Console.WriteLine("\nWorst ({0}):", fitness_Worst);
for (int i = 0; i < values_Worst.Length; i++)
{
Console.WriteLine("{0} ", values_Worst[i]);
}
var ar = new AlgoOutput()
{
Best = fitness_Best,
Worst = fitness_Worst,
Values_Best = values_Best,
Values_Worst = values_Worst,
};
return(ar);
}
private AlgoOutput RunAlgo()
{
// Crossover = 80%
// Mutation = 5%
// Population size = 100
// Generations = 2000
// Genome size = 2
GA ga = new GA(0.8, 0.05, 100, 5, 9, MaxMin.Minimize);
ga.FitnessFunction = new GAFunction(theActualFunction);
//ga.FitnessFile = @"E:\fitness.csv";
ga.Elitism = true;
ga.Go();
double[] values_Best;
double fitness_Best;
double[] values_Worst;
double fitness_Worst;
ga.GetBest(out values_Best, out fitness_Best);
Console.WriteLine("Best ({0}):", fitness_Best);
for (int i = 0; i < values_Best.Length; i++)
Console.WriteLine("{0} ", values_Best[i]);
ga.GetWorst(out values_Worst, out fitness_Worst);
Console.WriteLine("\nWorst ({0}):", fitness_Worst);
for (int i = 0; i < values_Worst.Length; i++)
Console.WriteLine("{0} ", values_Worst[i]);
var ar = new AlgoOutput()
{
Best=fitness_Best,
Worst=fitness_Worst,
Values_Best=values_Best,
Values_Worst=values_Worst,
};
return ar;
}
