protected EvolutionaryOptimization(
int genomLength,
int populationSize,
PointMutationPars mutationPars,
IComparer<Genom> fitnessComparer)
{
Contract.Requires(genomLength > 0);
Contract.Requires(populationSize > 1);
Contract.Requires(fitnessComparer != null);
GenomLength = genomLength;
PopulationSize = populationSize;
MutationPars = mutationPars;
FitnessComparer = new SmartComparer<Genom>(new GenomBagComparer(fitnessComparer));
}
public GA(
int genomLength,
int populationSize,
int crossoverPointNum,
PointMutationPars mutationPars,
ISelectionAlgorithm selectionAlgorithm,
IComparer<Genom> fitnessComparer) :
base(genomLength, populationSize, mutationPars, fitnessComparer)
{
Contract.Requires(genomLength > 0);
Contract.Requires(populationSize > 1);
Contract.Requires(crossoverPointNum > 0);
Contract.Requires(fitnessComparer != null);
Contract.Requires(selectionAlgorithm != null);
CrossoverPointNum = crossoverPointNum;
this.selectionAlgorithm = selectionAlgorithm;
}
public CrossEntropy(
int genomLength,
int populationSize,
double survivalPercent,
PointMutationPars mutationPars,
ISelectionAlgorithm selectionAlgorithm,
IComparer<Genom> fitnessComparer) :
base(genomLength, populationSize, mutationPars, fitnessComparer)
{
Contract.Requires(genomLength > 0);
Contract.Requires(populationSize > 1);
Contract.Requires(survivalPercent > 0.0 && survivalPercent < 100.0);
Contract.Requires(fitnessComparer != null);
Contract.Requires(selectionAlgorithm != null);
SurvivalPercent = survivalPercent;
this.selectionAlgorithm = selectionAlgorithm;
survivalCount = (int)Math.Round((double)PopulationSize * (SurvivalPercent / 100.0), MidpointRounding.AwayFromZero);
}
private static void RunGA()
{
const int validationSampleCount = 3;
var recipes = new ColorRecipes(@"recipe.xml");
int trainingSampleCount = recipes.Count - validationSampleCount;
var shuffled = recipes;//.OrderBy(r => RandomGenerator.Random.Next()).ToList();
var trainingComp = new ColorMixerGenomComparer(shuffled.Take(trainingSampleCount));
var validationComp = new ColorMixerGenomComparer(shuffled.Skip(trainingSampleCount));
var bestComp = new CombinedColorMixerGenomComprarer(trainingComp, validationComp);
// Algo
int valuesCount = new ColorMixingPars().ValuesCount + new ColorFilteringPars().ValuesCount;
// Cross Entropy
var selAlgo = new GaussianSelectionAlgorithm(.15);
var mp = new PointMutationPars(PointMutationType.Uniform, 0.005, 0.09);
var opt = new CrossEntropy(valuesCount, 200, 40, mp, selAlgo, trainingComp);
//// GA
//var selAlgo = new GaussianSelectionAlgorithm(.6);
////var selAlgo = new TournamentSelectionAlgorithm(10);
//var mp = new PointMutationPars(PointMutationType.Gaussian, 0.005, 0.025);
//var opt = new GA(valuesCount, 200, 10, mp, selAlgo, trainingComp);
// Harmony
//var wsAlgo = new GaussianSelectionAlgorithm(.1, SelectionDirection.FromBottom);
//var mp = new PointMutationPars(PointMutationType.Gaussian, 0.02, 0.005);
//var opt = new HarmonySearch(valuesCount, 200, 99.9, mp, wsAlgo, trainingComp);
opt.Initialize();
Genom best = null;
// Run
while (true)
{
opt.NextGeneration();
var current = opt[0];
bool bestChanged = false;
if (best == null)
{
best = current;
bestChanged = true;
}
else if (bestComp.Compare(current, best) < 0)
{
best = current;
bestChanged = true;
}
//if (opt.GenerationNo % opt.PopulationSize == 0)
{
Console.WriteLine("\n-- Generation {0} --", opt.GenerationNo);
Show("Current", current, trainingComp, validationComp);
Show("Best", best, trainingComp, validationComp, bestChanged);
}
}
}
