// In the GECCO paper, Section 2.1
public static double ImproveToLocalOptimum(BinaryProblem problem, BinaryVector solution, double fitness, IRandom rand)
{
var tried = new HashSet <int>();
do
{
var options = Enumerable.Range(0, solution.Length).Shuffle(rand);
foreach (var option in options)
{
if (tried.Contains(option))
{
continue;
}
solution[option] = !solution[option];
double newFitness = problem.Evaluate(solution, rand);
if (problem.IsBetter(newFitness, fitness))
{
fitness = newFitness;
tried.Clear();
}
else
{
solution[option] = !solution[option];
}
tried.Add(option);
}
} while (tried.Count != solution.Length);
return(fitness);
}
private static double Donate(BinaryVector solution, double fitness, BinaryVector source, IEnumerable <int> cluster, BinaryProblem problem, IRandom rand, out bool changed)
{
// keep track of which bits flipped to make the donation
List <int> flipped = new List <int>();
foreach (var index in cluster)
{
if (solution[index] != source[index])
{
flipped.Add(index);
solution[index] = !solution[index];
}
}
changed = flipped.Count > 0;
if (changed)
{
double newFitness = problem.Evaluate(solution, rand);
// if the original is strictly better, revert the change
if (problem.IsBetter(fitness, newFitness))
{
foreach (var index in flipped)
{
solution[index] = !solution[index];
}
}
else
{
// new solution is no worse than original, keep change to solution
fitness = newFitness;
}
}
return(fitness);
}
public override double Evaluate(BinaryVector vector, IRandom random)
{
if (Evaluations >= maxEvaluations)
{
throw new OperationCanceledException("Maximum Evaluation Limit Reached");
}
Evaluations++;
double fitness = problem.Evaluate(vector, random);
if (double.IsNaN(BestQuality) || problem.IsBetter(fitness, BestQuality))
{
BestQuality = fitness;
BestSolution = (BinaryVector)vector.Clone();
BestFoundOnEvaluation = Evaluations;
}
return(fitness);
}
private static double Donate(BinaryVector solution, double fitness, BinaryVector source, IEnumerable<int> cluster, BinaryProblem problem, IRandom rand, out bool changed) {
// keep track of which bits flipped to make the donation
List<int> flipped = new List<int>();
foreach (var index in cluster) {
if (solution[index] != source[index]) {
flipped.Add(index);
solution[index] = !solution[index];
}
}
changed = flipped.Count > 0;
if (changed) {
double newFitness = problem.Evaluate(solution, rand);
// if the original is strictly better, revert the change
if (problem.IsBetter(fitness, newFitness)) {
foreach (var index in flipped) {
solution[index] = !solution[index];
}
} else {
// new solution is no worse than original, keep change to solution
fitness = newFitness;
}
}
return fitness;
}
// In the GECCO paper, Section 2.1
public static double ImproveToLocalOptimum(BinaryProblem problem, BinaryVector solution, double fitness, IRandom rand) {
var tried = new HashSet<int>();
do {
var options = Enumerable.Range(0, solution.Length).Shuffle(rand);
foreach (var option in options) {
if (tried.Contains(option)) continue;
solution[option] = !solution[option];
double newFitness = problem.Evaluate(solution, rand);
if (problem.IsBetter(newFitness, fitness)) {
fitness = newFitness;
tried.Clear();
} else {
solution[option] = !solution[option];
}
tried.Add(option);
}
} while (tried.Count != solution.Length);
return fitness;
}
