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); }