// Worker thread
void SearchSolution()
{
// create fitness function
SymbolicRegressionFitness fitness = new SymbolicRegressionFitness(data, new double[] { 1, 2, 3, 5, 7 });
// create gene function
IGPGene gene = (functionsSet == 0) ?
(IGPGene)new SimpleGeneFunction(6) :
(IGPGene)new ExtendedGeneFunction(6);
// create population
Population population = new Population(populationSize,
(geneticMethod == 0) ?
(IChromosome)new GPTreeChromosome(gene) :
(IChromosome)new GEPChromosome(gene, 15),
fitness,
(selectionMethod == 0) ? (ISelectionMethod)new EliteSelection() :
(selectionMethod == 1) ? (ISelectionMethod)new RankSelection() :
(ISelectionMethod)new RouletteWheelSelection()
);
// iterations
int i = 1;
// solution array
double[,] solution = new double[50, 2];
double[] input = new double[6] { 0, 1, 2, 3, 5, 7 };
// calculate X values to be used with solution function
for (int j = 0; j < 50; j++)
{
solution[j, 0] = chart.RangeX.Min + (double)j * chart.RangeX.Length / 49;
}
// loop
while (!needToStop)
{
// run one epoch of genetic algorithm
population.RunEpoch();
try
{
// get best solution
string bestFunction = population.BestChromosome.ToString();
// calculate best function
for (int j = 0; j < 50; j++)
{
input[0] = solution[j, 0];
solution[j, 1] = PolishExpression.Evaluate(bestFunction, input);
}
chart.UpdateDataSeries("solution", solution);
// calculate error
double error = 0.0;
for (int j = 0, k = data.GetLength(0); j < k; j++)
{
input[0] = data[j, 0];
error += Math.Abs(data[j, 1] - PolishExpression.Evaluate(bestFunction, input));
}
// set current iteration's info
SetText(currentIterationBox, i.ToString());
SetText(currentErrorBox, error.ToString("F3"));
}
catch
{
// remove any solutions from chart in case of any errors
chart.UpdateDataSeries("solution", null);
}
// increase current iteration
i++;
//
if ((iterations != 0) && (i > iterations))
break;
}
// show solution
SetText(solutionBox, population.BestChromosome.ToString());
// enable settings controls
EnableControls(true);
}
