public static void Run(CostFunction f, int max_iteration = 500)
{
ConjugateGradientSearch s = new ConjugateGradientSearch();
double[] x_0 = f.CreateRandomSolution();
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
s.Minimize(x_0, f, max_iteration);
}
public DifferentialEvolution(CostFunction f)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize = f.DimensionCount * 10;
mDimension = f.DimensionCount;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}
public static void Run(CostFunction f, int max_iterations = 200)
{
SLOP s = new SLOP();
double[] x_0 = f.CreateRandomSolution();
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
s.Minimize(x_0, f, max_iterations);
}
public EvolutionaryProgramming(CostFunction f, int popSize = 100, int boutSize = 5)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize = popSize;
mDimension = f.DimensionCount;
mBoutSize = boutSize;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}
public EvolutionStrategy(CostFunction f, int mu = 30, int lambda = 20)
{
mSolutionGenerator = (dimension, constraints) =>
{
double[] x_0 = f.CreateRandomSolution();
return(x_0);
};
mPopSize_mu = mu;
mOffspringSize_lambda = lambda;
mDimension = f.DimensionCount;
mUpperBounds = f.UpperBounds;
mLowerBounds = f.LowerBounds;
}