public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
DifferentialEvolution s = new DifferentialEvolution(f);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
int popSize = 100; // population Size
EvolutionaryProgramming s = new EvolutionaryProgramming(f, popSize);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
int mu = 30;
int lambda = 20;
EvolutionStrategy s = new EvolutionStrategy(f, mu, lambda);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public static void RunRosenbrockSaddle(int max_iterations = 500)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
Run(f, max_iterations);
}
