private void Go(object sender, DoWorkEventArgs e)
{
bool[,] iterationSolution;
bool needToStop = false;
for (int j = 4; j < 8; j++)
{
iterationSolution = GetIterationSolution(j);
EvolutiveFitnessFunction fitness = new EvolutiveFitnessFunction(iterationSolution);
ITreeGene gene = (ITreeGene)new BooleanFunction(4);
if ((Selection is IMixedSelection) || (Replacement is IMixedSelection))
{
Population = new Population(Individuals,
(IIndividual)new TreeIndividual(gene),
fitness, Selection, Replacement, Iterations,
MutationRate, SelectionFirstCount, ReplacementFirstCount,
SelectionSecondCount, ReplacementSecondCount);
}
else
{
Population = new Population(Individuals,
(IIndividual)new TreeIndividual(gene),
fitness, Selection, Replacement, Iterations,
MutationRate, SelectionFirstCount, ReplacementFirstCount);
}
Population.GenerationEnded += new Population.GenerationEndedEventHandler(OnGenerationEnded);
int i = 1;
while (!needToStop)
{
//corremos la simulacion para una generacion
population.RunEpoch();
population.OnGenerationEnded(new GenerationEndedEventArgs(population, i));
try
{
string bestFunction = population.BestIndividual.ToString();
OutputLine(bestFunction);
}
catch (Exception) { }
i++;
if ((Iterations != 0) && (i > Iterations))
needToStop = true;
}
needToStop = false;
bool[] variables = new bool[4];
OutputLine("bit " + (3 - (j - 4)).ToString());
for (int k = 0; k < iterationSolution.GetLength(0); k++)
{
variables[0] = iterationSolution[k, 0];
variables[1] = iterationSolution[k, 1];
variables[2] = iterationSolution[k, 2];
variables[3] = iterationSolution[k, 3];
OutputLine(iterationSolution[k, 0].ToString() + iterationSolution[k, 1].ToString() + iterationSolution[k, 2].ToString() + iterationSolution[k, 3].ToString() + " = " +
PolishBooleanExpression.Evaluate(population.BestSolution.ToString(), variables).ToString()
);
}
OutputLine(population.BestSolution.ToString());
OutputLine(PolishBooleanExpression.ToInfix(population.BestSolution.ToString(), variables));
OutputLine("Aptitud: " + population.BestSolution.Fitness);
OutputLine("Es correcta: " + ((population.BestSolution.Fitness > 69000.00 - population.BestSolution.Fitness.ToString().Length) ? "Si" : "No"));
SetSolution((3 - (j - 4)), PolishBooleanExpression.ToInfix(population.BestSolution.ToString(), variables));
}
lblStatus.Text = "Finalizado";
}
private void Go()
{
bool[,] truthTable = new bool[16, 8]{
{false,false,false,false,false,false,false,false},
{false,false,false,true,false,false,false,false},
{false,false,true,false,false,false,false,false},
{false,false,true,true,false,false,false,false},
{false,true,false,false,false,false,false,false},
{false,true,false,true,false,false,false,true},
{false,true,true,false,false,false,true,false},
{false,true,true,true,false,false,true,true},
{true,false,false,false,false,false,false,false},
{true,false,false,true,false,false,true,false},
{true,false,true,false,false,true,false,false},
{true,false,true,true,false,true,true,false},
{true,true,false,false,false,false,false,false},
{true,true,false,true,false,false,true,true},
{true,true,true,false,false,true,true,false},
{true,true,true,true,true,false,false,true}
};
bool[,] iterationSolution;
for (int j = 4; j < 8; j++)
{
iterationSolution = new bool[16, 5]{
{false,false,false,false,truthTable[0,j]},
{false,false,false,true,truthTable[1,j]},
{false,false,true,false,truthTable[2,j]},
{false,false,true,true,truthTable[3,j]},
{false,true,false,false,truthTable[4,j]},
{false,true,false,true,truthTable[5,j]},
{false,true,true,false,truthTable[6,j]},
{false,true,true,true,truthTable[7,j]},
{true,false,false,false,truthTable[8,j]},
{true,false,false,true,truthTable[9,j]},
{true,false,true,false,truthTable[10,j]},
{true,false,true,true,truthTable[11,j]},
{true,true,false,false,truthTable[12,j]},
{true,true,false,true,truthTable[13,j]},
{true,true,true,false,truthTable[14,j]},
{true,true,true,true,truthTable[15,j]}
};
EvolutiveFitnessFunction fitness = new EvolutiveFitnessFunction(iterationSolution);
ITreeGene gene = (ITreeGene)new BooleanFunction(4);
Population population = new Population(populationSize,
(IIndividual)new TreeIndividual(gene),
fitness, (ISelection)new EliteWheelMixedSelection(),new EliteWheelMixedSelection(), 100, 0.005,20,40,20, 80);
int i = 1;
while (!needToStop)
{
population.RunEpoch();
try
{
string bestFunction = population.BestIndividual.ToString();
Debug.WriteLine(bestFunction);
}
catch (Exception)
{
}
i++;
//
if ((iterations != 0) && (i > iterations))
break;
}
bool[] variables = new bool[4];
Debug.WriteLine("solution " + j.ToString());
for (int k = 0; k < iterationSolution.GetLength(0); k++)
{
variables[0] = iterationSolution[k, 0];
variables[1] = iterationSolution[k, 1];
variables[2] = iterationSolution[k, 2];
variables[3] = iterationSolution[k, 3];
Debug.WriteLine(iterationSolution[k, 0].ToString() + iterationSolution[k, 1].ToString() + iterationSolution[k, 2].ToString() + iterationSolution[k, 3].ToString() + " = " +
PolishBooleanExpression.Evaluate(population.BestSolution.ToString(), variables).ToString()
);
}
Debug.WriteLine(population.BestIndividual.ToString());
}
}
