/// <summary>
/// make a clone of the given individual
/// </summary>
/// <param name="source"></param>
public void Copy(selfoptInstance source)
{
for (int p = 0; p < parameter.Length; p++)
{
parameter[p].Copy(source.parameter[p]);
}
score = source.score;
}
private void init(int no_of_instances, int parameters_per_instance)
{
this.parameters_per_instance = parameters_per_instance;
instance = new selfoptInstance[no_of_instances];
for (int i = 0; i < no_of_instances; i++)
instance[i] = new selfoptInstance(parameters_per_instance);
parameterName = new String[parameters_per_instance];
// store a few best scores
bestScoreHistory = new float[1000];
scoreHistory = new float[1000];
// create an array to keep track of phase space scores
phase_space_scores = new float[50, 50, 2];
}
/// <summary>
/// perform crossover between two parent individuals
/// </summary>
/// <param name="parent1"></param>
/// <param name="parent2"></param>
/// <param name="mutation_rate"></param>
/// <param name="rnd"></param>
public void copysexual(selfoptInstance parent1, selfoptInstance parent2, float mutation_rate, Random rnd)
{
//copy from first parent
Copy(parent1);
//copy half the parameters randomly from second parent
for (int p = 0; p < parameter.Length / 2; p++)
{
int idx = rnd.Next(parameter.Length - 1);
parameter[idx].Copy(parent2.parameter[idx]);
}
//mutate
score = 1;
Mutate(rnd, mutation_rate);
score = 0;
}
private void init(int no_of_instances, int parameters_per_instance)
{
this.parameters_per_instance = parameters_per_instance;
instance = new selfoptInstance[no_of_instances];
for (int i = 0; i < no_of_instances; i++)
{
instance[i] = new selfoptInstance(parameters_per_instance);
}
parameterName = new String[parameters_per_instance];
// store a few best scores
bestScoreHistory = new float[1000];
scoreHistory = new float[1000];
// create an array to keep track of phase space scores
phase_space_scores = new float[50, 50, 2];
}
/// <summary>
/// sort into score order, best first
/// </summary>
private void Sort()
{
for (int i = 0; i < instance.Length - 1; i++)
{
for (int j = i + 1; j < instance.Length; j++)
{
if (((smallerScoresAreBetter) && (instance[i].score > instance[j].score)) ||
((!smallerScoresAreBetter) && (instance[i].score < instance[j].score)))
{
selfoptInstance temp = instance[i];
instance[i] = instance[j];
instance[j] = temp;
}
}
}
best_index = 0;
best_score = instance[best_index].score;
}
/// <summary>
/// proceed to the next generation
/// </summary>
private void update()
{
// sort by score
Sort();
// replace lower scoring instances, using sexual reproduction
for (int i = instance.Length / 2; i < instance.Length; i++)
{
selfoptInstance parent1 = instance[rnd.Next(instance.Length / 2)];
selfoptInstance parent2 = instance[rnd.Next(instance.Length / 2)];
instance[i].copysexual(parent1, parent2, mutationRate, rnd);
}
//set the current instance to be evaluated
current_instance_index = instance.Length / 2;
// increment generations
generation++;
}
/// <summary>
/// perform crossover between two parent individuals
/// </summary>
/// <param name="parent1"></param>
/// <param name="parent2"></param>
/// <param name="mutation_rate"></param>
/// <param name="rnd"></param>
public void copysexual(selfoptInstance parent1, selfoptInstance parent2, float mutation_rate, Random rnd)
{
//copy from first parent
Copy(parent1);
//copy half the parameters randomly from second parent
for (int p = 0; p < parameter.Length / 2; p++)
{
int idx = rnd.Next(parameter.Length - 1);
parameter[idx].Copy(parent2.parameter[idx]);
}
//mutate
score = 1;
Mutate(rnd, mutation_rate);
score = 0;
}
/// <summary>
/// make a clone of the given individual
/// </summary>
/// <param name="source"></param>
public void Copy(selfoptInstance source)
{
for (int p = 0; p < parameter.Length; p++)
parameter[p].Copy(source.parameter[p]);
score = source.score;
}
