/// <summary>
/// Called before Optimize() to setup the optimizer.
/// Always called in main thread which would allow for putting additional GUI in here.
/// </summary>
public override void Initialize()
{
// don't forget to call Initialize() on the base class before any custom handling
base.Initialize();
bool optionWindowRequired = false;
StrategyParameters = new Chromosomes(Strategy, true);
if (StrategyParameters.HasBoolOrEnum)
{
optionWindowRequired = true;
if (options == null)
{
options = new GoForm(StrategyParameters);
}
}
if (optionWindowRequired && options.ShowDialog(Form.ActiveForm) != DialogResult.OK)
{
StrategyParameters = new Chromosomes(Strategy, true);
}
}
/// <summary>
/// Runs the optimizer on a given parameter set.
/// Beware: this method might be called in a non-main, worker thread. E.g. driving GUI logic from here forward would cause issues.
/// </summary>
public override void Optimize()
{
Individual supremeIndividual = null;
try
{
var realStabilitySize = (int)Math.Ceiling(populationSize * stabilitySize);
var realResetSize = (int)Math.Ceiling(populationSize * resetSize);
var numberOfStrategyParameters = StrategyParameters.Count;
var combinations = StrategyParameters.CountCombinations;
var siblings = new Dictionary <Individual, bool>();
var children = new List <Individual>();
var parents = new List <Individual>();
var prevStabilityScore = double.NaN;
// Start optimization process
for (int generation = 1; generation <= maxGenerations; generation++)
{
//If Initial generation, seed the population with random individuals
while (children.Count < populationSize && siblings.Count < combinations)
{
ValidateIndividual(children, siblings, CreateRandomIndividual());
}
foreach (Individual individual in children)
{
if (UserAbort)
{
throw new AbortException();
}
StrategyParameters.SetStrategyParameters(individual, Strategy, Parameters);
RunIteration(IterationCompleted, individual);
}
WaitForIterationsCompleted(false); //Make sure that each individual completed its iteration
parents.AddRange(children);
parents.Sort();
Individual generationBest = parents[0]; //This is the best performing individual since stability reset
if (parents.Count > 0 && generationBest != null &&
(supremeIndividual == null || supremeIndividual.Performance < generationBest.Performance))
{
supremeIndividual = generationBest;
}
int stabilityIndividuals = Math.Max(1, Math.Min(parents.Count, realStabilitySize));
double stabilityScore = 0.0;
for (int i = 0; i < stabilityIndividuals; i++)
{
stabilityScore += parents[i].Performance;
}
if (supremeIndividual != null && minimumPerformance > 0 && supremeIndividual.Performance > minimumPerformance)
{
break;
}
children.Clear(); //Clearing up space for the next generation
if (siblings.Count >= combinations || generation == maxGenerations)
{
break;
}
//Repopulating the next generation.
//If Population is stable, leave just best performing individuals
if (stabilityScore == prevStabilityScore)
{
if (realResetSize <= 0)
{
parents.Clear();
}
else
if (parents.Count > 0)
{
parents = parents.GetRange(0, Math.Min(parents.Count, realResetSize));
}
continue;
}
//Assign a weight for each parent
RankPopulation(parents);
//Reproduce extra individuals mutated from the best
ShakeBestPerformer(children, siblings, combinations, generationBest, numberOfStrategyParameters);
//Reproduce children using Crossover, Mutation and totally random genomes
while (children.Count < populationSize && siblings.Count < combinations)
{
if (UserAbort)
{
throw new AbortException();
}
if (random.NextDouble() <= crossoverRate)
{
int[] chromosomeIndexesForSon;
int[] chromosomeIndexesForDaughter;
//Produce two children from two parents
//Parents are selected using Roulette Selection Method
//Note: There are many other selection algorithms (Google them out...)
//TODO: Implements some additional selection methods
//Crossover is performed using single break point
//TODO: implement 2 or 3 break points Crossover algorithms
Crossover(numberOfStrategyParameters, parents[RouletteSelection(parents)], parents[RouletteSelection(parents)], out chromosomeIndexesForSon, out chromosomeIndexesForDaughter);
for (var idx = 0; idx < numberOfStrategyParameters; idx++)
{
if (StrategyParameters[idx].Count <= 1)
{
continue;
}
//After children are produced from crossover, small part should be mutated
if (random.NextDouble() <= mutationRate)
{
chromosomeIndexesForSon[idx] = Mutate(mutationStrength, StrategyParameters[idx].Count, chromosomeIndexesForSon[idx]);
}
if (random.NextDouble() <= mutationRate)
{
chromosomeIndexesForDaughter[idx] = Mutate(mutationStrength, StrategyParameters[idx].Count, chromosomeIndexesForDaughter[idx]);
}
}
//Make sure that children are unique, so we wont waste any time for evaluation of their performance
ValidateIndividual(children, siblings, new Individual(chromosomeIndexesForSon));
ValidateIndividual(children, siblings, new Individual(chromosomeIndexesForDaughter));
}
else
{
//Create random children
//Make sure that children are unique, so we wont waste any time for evaluation of their performance
ValidateIndividual(children, siblings, CreateRandomIndividual());
ValidateIndividual(children, siblings, CreateRandomIndividual());
}
}
prevStabilityScore = stabilityScore;
}
}
catch
{
}
finally
{
WaitForIterationsCompleted(true);
if (options != null)
{
options.Dispose();
options = null;
}
}
}
