public LocalityTest(GrammaticalEvolution ge1, GrammaticalEvolution ge2, FitnessCalculator calculator, ExecutionParameters parameters)
{
GE1 = ge1;
GE2 = ge2;
Calculator = calculator;
Parameters = parameters;
}
private void Evaluate(Generation generation)
{
foreach (Chromosome chromosome in generation)
{
if (chromosome.Fitness.HasValue && !chromosome.ReEvaluate)
{
continue;
}
generation.Stats.executedEvaluations++;
chromosome.ReEvaluate = false;
GrammaticalEvolution newGE = GE.GetClone();
string code = null;
object result = null;
Exception generationException = null;
CompilerErrorCollection compilationErrors = null;
Exception executionException = null;
try
{
code = newGE.Generate(chromosome, generation.Stats);
}
catch (GrammaticalEvolution.ErrorCorrectionFailedException e)
{
generationException = e;
generation.Stats.generationErrors++;
}
catch (GrammaticalEvolution.EndOfCodonQueueException e)
{
generationException = e;
generation.Stats.generationErrors++;
}
if (generationException == null)
{
try
{
result = Execute(code);
}
catch (Executor.CompilationErrorException e)
{
compilationErrors = e.Errors;
generation.Stats.compilationErrors++;
}
catch (Executor.ExecutionExceptionException e)
{
executionException = e.InnerException;
generation.Stats.executionExceptions++;
}
}
chromosome.Fitness = _FitnessCalculator(result, generationException, compilationErrors, executionException);
if (generationException == null && compilationErrors == null && executionException == null)
{
chromosome.BackupCodons = chromosome.ToList();//If the compilation was successful the codons are copied to the backupCodons
}
}
}
public GeneticAlgorithm(int elitismSize, GrammaticalEvolution ge, FitnessCalculator fitnessCalculator, Selection selection, Crossover crossover, Mutation mutation, Population population, ExecutionParameters parameters)
{
ElitismSize = elitismSize;
GE = ge;
_FitnessCalculator = fitnessCalculator;
Selection = selection;
Crossover = crossover;
Mutation = mutation;
Population = population;
Parameters = parameters;
}
public Tuple <List <double>, List <double> > ExecuteTest(int testnumber, int testSize, int mutatedIndividuals, int initialChromosomeSize, double failureFitnessValue, bool useGE1ForBase, bool includeFailedCorrections)
{
List <double> localityResults1 = new List <double>();
List <double> localityResults2 = new List <double>();
for (int i = 0; i < testSize; i++)
{
Chromosome baseChromosome = null;
double baseFitness = failureFitnessValue;
while (baseFitness == failureFitnessValue)//Check that it is a correct individual
{
baseChromosome = new Chromosome(initialChromosomeSize, 64);
GrammaticalEvolution baseGE = (useGE1ForBase ? GE1 : GE2).GetClone();
baseFitness = Evaluate(baseChromosome, true, baseGE).Item1;
}
for (int j = 0; j < mutatedIndividuals; j++)//TODO: Perché le differenze del 2 è di 1?.
{
Chromosome mutatedChromosome1 = SingleMutation(baseChromosome);
mutatedChromosome1.BackupCodons = baseChromosome.ToList();
Chromosome mutatedChromosome2 = mutatedChromosome1.GetClone();
Tuple <double, bool> result1 = Evaluate(mutatedChromosome1, false, GE1);
Tuple <double, bool> result2 = Evaluate(mutatedChromosome2, false, GE2);
double minDistance = (double)1 / 64;
localityResults1.Add(Math.Abs(result1.Item1 - baseFitness - minDistance));
double secondResult = result2.Item2 ? baseFitness : result2.Item1;
localityResults2.Add(Math.Abs(secondResult - baseFitness - minDistance));
}
Console.WriteLine(i + " out of " + testSize);
}
Console.WriteLine("Media 1:" + localityResults1.Average());
Console.WriteLine("Media 2:" + localityResults2.Average());
Console.WriteLine("Miglioramento:" + ((localityResults1.Average() - localityResults2.Average()) / localityResults1.Average()));
return(new Tuple <List <double>, List <double> >(localityResults1, localityResults2));
/*string percorso = @"C:\Users\Samuele\Documents\BetterPECLE\v3\Studio località\";
* //string avviso = "ATTENZIONE: QUESTI RISULTATI SONO STATI DEFINITI PREVENTIVAMENTE COME TEST.\n" +
* // "AL FINE DI RISPETTARE L'ETICA SCIENTIFICA, NON USARE QUESTI DATI IN NESSUNA PUBBLICAZIONE.\n\n";
*
* double miglioramento = ((localityResults1.Average() - localityResults2.Average()) / localityResults1.Average());
*
* string spiegazione = "I seguenti dati indicano la differenza di fitness medio per mutazione. Più la differenza\n" +
* "è bassa, più la rappresentazione è locale.\n\n";
* File.WriteAllText(percorso + "Studio" + testnumber + ".txt",
* spiegazione + "Media default: " + localityResults1.Average() +
* "\nDeviazione Standard: " + StdDev(localityResults1) +
* "\nMedia PECLE: " + localityResults2.Average() +
* "\nDeviazione Standard: " + StdDev(localityResults2) +
* "\nMiglioramento: " + miglioramento);*/
}
private static GeneticAlgorithmResult PECLETest_NoImprovements(int populationSize, int generationNumber, int initialChromosomeSize, int maximumChromosomeSize, int elitismSize, int tournamentSize, double crossoverProbability, double mutationProbability, double duplicationProbability, double pruningProbability)
{
GrammaticalEvolution ge = new GrammaticalEvolution();
ge.BluePrintGOs = new List <GrammaticalObject>()
{
new OneMaxGO_NoImprovements()
};
ge.StartingGO = new OneMaxGO_NoImprovements();
ge.StartingProductionRule = new ProductionRule(new NonTerminal("code"));
GeneticAlgorithm ga = new GeneticAlgorithm(elitismSize, ge, Calculator, new TournamentSelection(tournamentSize, false), new OnePointCrossover(), new UniformMutation(), new Population(populationSize, initialChromosomeSize, maximumChromosomeSize), new ExecutionParameters("namespace A{ public class B{ public bool[] C() { \nbool[] grid = new bool[64];\n int a = 0;\n int b = 0;\n int c = 0;\n PECLECODE\n return grid;}}}", "A.B", "C"));
return(ga.Evolve(generationNumber, crossoverProbability, mutationProbability, duplicationProbability, pruningProbability));
}
private Tuple <double, bool> Evaluate(Chromosome chromosome, bool saveBackupCodons, GrammaticalEvolution ge)
{
GrammaticalEvolution newGE = ge.GetClone();
string code = null;
object result = null;
Exception generationException = null;
CompilerErrorCollection compilationErrors = null;
Exception executionException = null;
GenerationStats s = new GenerationStats();
try
{
code = newGE.Generate(chromosome, s);
}
catch (GrammaticalEvolution.ErrorCorrectionFailedException e)
{
generationException = e;
}
catch (GrammaticalEvolution.EndOfCodonQueueException e)
{
generationException = e;
}
if (generationException == null)
{
try
{
result = Executor.Execute(Parameters, code);
}
catch (Executor.CompilationErrorException e)
{
compilationErrors = e.Errors;
}
catch (Executor.ExecutionExceptionException e)
{
executionException = e.InnerException;
}
}
if (generationException == null && compilationErrors == null && executionException == null && saveBackupCodons)
{
chromosome.BackupCodons = chromosome.ToList();
}
return(new Tuple <double, bool>(Calculator(result, generationException, compilationErrors, executionException), s.failedErrorCorrections > 0));
}
public GrammaticalEvolution GetClone()
{
GrammaticalEvolution ge = new GrammaticalEvolution();
if (BluePrintGOs != null)
{
ge.BluePrintGOs = new List <GrammaticalObject>();
foreach (GrammaticalObject go in BluePrintGOs)
{
ge.BluePrintGOs.Add(go.GetClone());
}
}
ge.codonPosition = codonPosition;
ge.StartingGO = StartingGO == null ? null : StartingGO.GetClone();
ge.StartingProductionRule = StartingProductionRule == null ? null : new ProductionRule(StartingProductionRule.ToArray());
ge._currentGenerationStats = _currentGenerationStats == null ? null : _currentGenerationStats.GetClone();
ge._genome = _genome == null ? null : _genome.GetClone();
return(ge);
}
