/// <summary>
/// Configure the Genetic Algorithm.
/// </summary>
/// <param name="ga">The genetic algorithm.</param>
public virtual void ConfigGATermination(IEVA ga)
{
GA = ga;
ga.TerminationReached += (sender, args) =>
{ /*/
* using (var collection = new MagickImageCollection())
* {
* var files = Directory.GetFiles(m_destFolder, "*.png");
*
* foreach (var image in files)
* {
* collection.Add(image);
* collection[0].AnimationDelay = 100;
* }
*
* var settings = new QuantizeSettings();
* settings.Colors = 256;
* collection.Quantize(settings);
*
* collection.Optimize();
* collection.Write(Path.Combine(m_destFolder, "result.gif"));
* }
* /**/
Saveinfo();
};
}
/// <summary>
/// Configuration GA.
/// </summary>
/// <param name="problemConfig">Problem represention</param>
/// <returns>GA</returns>
public static IEVA[] SetGA(IProblemConfig[] problemConfig, bool parlelization)
{
Console.WriteLine("Custom Genetic algorithm.");
var termination = ParameterSetter.SetTermination(problemConfig[0]);
var crossover = ParameterSetter.SetXover(problemConfig[0]);
var xoverProb = ParameterSetter.SetFloat("Xover Probability", 0, 1);
var mutation = ParameterSetter.SetMutation(problemConfig[0]);
var mutationProb = ParameterSetter.SetFloat("Mutation Probability", 0, 1);
var selection = ParameterSetter.SetSelection(problemConfig[0]);
var elitizmus = ParameterSetter.SetElite(problemConfig[0]);
var executor = problemConfig[0].CreateExecutor();
var popSize = ParameterSetter.SetPositiveIntParameter("Pop size", 2);
IEVA[] evas = new IEVA[problemConfig.Length];
int i = 0;
foreach (var config in problemConfig)
{
// creates population with curr. popSize
var population = new Population(popSize, config.CreateIndividual, config.CreateEmptyIndividual);
// new operators
var fitness = config.CreateFitness();
evas[i++] = new GeneticAlgorithm(population, fitness, selection, crossover, mutation, elitizmus, termination, executor, mutationProb, xoverProb);
}
return(evas);
}
/// <summary>
/// Determines whether the specified genetic algorithm fulfilled the termination condition.
/// </summary>
/// <param name="eva">The genetic algorithm.</param>
/// <returns>True if termination has been fulfilled, otherwise false.</returns>
public bool IsFulfilled(IEVA eva)
{
if (eva.TimeEvolving < maxTime)
{
return(false);
}
return(true);
}
/// <summary>
/// Determines whether the specified genetic algorithm fulfilled the termination condition.
/// </summary>
/// <param name="eva">The genetic algorithm.</param>
/// <returns>True if termination has been fulfilled, otherwise false.</returns>
public bool IsFulfilled(IEVA eva)
{
if (eva.CurrentGenerationsNumber < expectedMaxGeneration)
{
return(false);
}
return(true);
}
/// <summary>
/// Console logger.
/// </summary>
/// <param name="eva">evolutionary alg.</param>
/// <param name="consoleProblemConfig">Problem represention</param>
/// <param name="logRate">logger rate</param>
public void SetGenerationInfoLog(IEVA eva, IProblemConfig consoleProblemConfig, int logRate)
{
eva.CurrentGenerationInfo += delegate
{
var bestIndividual = eva.BestIndividual;
Console.WriteLine("Generations: {0}", eva.CurrentGenerationsNumber);
Console.WriteLine("Fitness: {0,10}", bestIndividual.Fitness);
Console.WriteLine("Time: {0}", eva.TimeEvolving);
var speed = eva.TimeEvolving.TotalSeconds / eva.CurrentGenerationsNumber;
Console.WriteLine("Speed (gen/sec): {0:0.0000}", speed);
var best = consoleProblemConfig.ShowBestIndividual(bestIndividual, logRate);
var fit = eva.CurrentGenerationsNumber + ";" + (bestIndividual.Fitness);
var elapsedTimeSpeed = eva.CurrentGenerationsNumber + ";" + speed;
var allInfo = eva.CurrentGenerationsNumber + ";" + (bestIndividual.Fitness) + ";" + eva.TimeEvolving + ";" + speed;
consoleProblemConfig.SetGenerationInfo(fit, elapsedTimeSpeed, allInfo);
};
}
/// <summary>
/// Configuration ES.
/// </summary>
/// <param name="problemConfig">Problem represention</param>
/// <returns>ES</returns>
public static IEVA[] SetES(IProblemConfig[] problemConfig, bool parallelization)
{
Console.WriteLine("Evolutionary Strategy.");
var evaName = ParameterSetter.SetEvaES();
// CMAES - internal population size setter
var popSize = 20;
IEVA[] evas = new IEVA[problemConfig.Length];
int i = 0;
foreach (var config in problemConfig)
{
// creates population with curr. popSize
var population = new Population(popSize, config.CreateIndividual, config.CreateEmptyIndividual);
// new operators
var fitness = config.CreateFitness();
// Delegate.CreateDelegate(consoleProblemConfig.CreateIndividual);
evas[i++] = Activator.CreateInstance(evaName, fitness, population) as IEVA;
}
return(evas);
}
/// <summary>
/// Parallel EA.
/// </summary>
/// <param name="problemConfigName">Problem represention.</param>
/// <param name="inputFileName">Input image</param>
/// <param name="logRate">logger rate</param>
public static void RunParallelImage(Type problemConfigName, string inputFileName, int logRate, bool parallelization)
{
var img = Image.FromFile(inputFileName) as Bitmap;
var blockSize = ParameterSetter.SetPositiveIntParameter("Block size", 5);
Bitmap[] targets = Split_Concate.SplitImg(img, blockSize);
var consoleProblemConfigs = new IImageProblemConfig[targets.Length];
var evas = new IEVA[targets.Length];
var configuration = new Configuration[targets.Length];
for (int i = 0; i < targets.Length; i++)
{
consoleProblemConfigs[i] = Activator.CreateInstance(problemConfigName) as IImageProblemConfig;
consoleProblemConfigs[i].Initialize(i);
consoleProblemConfigs[i].Initialize(targets[i], inputFileName);
configuration[i] = new Configuration();
}
evas = Configuration.SetEVA(consoleProblemConfigs, parallelization);
for (int i = 0; i < targets.Length; i++)
{
configuration[i].SetGenerationInfoLog(evas[i], consoleProblemConfigs[i], logRate);
consoleProblemConfigs[i].ConfigGATermination(evas[i]);
}
IIndividual[] bestInds = new IIndividual[targets.Length];
var stopwatch = Stopwatch.StartNew();
/**/
Parallel.For(0, evas.Length, index => {
evas[index].Run();
bestInds[index] = evas[index].BestIndividual;
// configuration[index].Saveinfo(inputFileName, index);
});
/*/
*
* for (var index = 0; index < evas.Length; index++)
* {
* evas[index].Run();
*
* bestInds[index] = evas[index].BestIndividual;
*
* // Configuration.Saveinfo(inputFileName,);
*
* }
*
*
* /**/
stopwatch.Stop();
Console.WriteLine("Total time: " + stopwatch.Elapsed);
var result = Split_Concate.ConcateImgs(bestInds, blockSize, img.Width, img.Height);
var m_destFolder = Configuration.CreateDirectory(inputFileName, "result_final");
var fileName = m_destFolder + "/" + "final" + ".png";
result.Save(fileName);
}
