Accord.Genetic.Population.RunEpoch C# (CSharp) Method

        public void RunEpoch()
        {
            Crossover();
            Mutate();
            Selection();

            if (autoShuffling)
                Shuffle();
        }

        // Worker thread
        void SearchSolution()
        {
            // constants
            double[] constants = new double[10] { 1, 2, 3, 5, 7, 11, 13, 17, 19, 23 };
            // create fitness function
            TimeSeriesPredictionFitness fitness = new TimeSeriesPredictionFitness(
                data, windowSize, predictionSize, constants);
            // create gene function
            IGPGene gene = (functionsSet == 0) ?
                (IGPGene)new SimpleGeneFunction(windowSize + constants.Length) :
                (IGPGene)new ExtendedGeneFunction(windowSize + constants.Length);
            // create population
            Population population = new Population(populationSize,
                (geneticMethod == 0) ?
                (IChromosome)new GPTreeChromosome(gene) :
                (IChromosome)new GEPChromosome(gene, headLength),
                fitness,
                (selectionMethod == 0) ? (ISelectionMethod)new EliteSelection() :
                (selectionMethod == 1) ? (ISelectionMethod)new RankSelection() :
                (ISelectionMethod)new RouletteWheelSelection()
                );
            // iterations
            int i = 1;
            // solution array
            int solutionSize = data.Length - windowSize;
            double[,] solution = new double[solutionSize, 2];
            double[] input = new double[windowSize + constants.Length];

            // calculate X values to be used with solution function
            for (int j = 0; j < solutionSize; j++)
            {
                solution[j, 0] = j + windowSize;
            }
            // prepare input
            Array.Copy(constants, 0, input, windowSize, constants.Length);

            // loop
            while (!needToStop)
            {
                // run one epoch of genetic algorithm
                population.RunEpoch();

                try
                {
                    // get best solution
                    string bestFunction = population.BestChromosome.ToString();

                    // calculate best function and prediction error
                    double learningError = 0.0;
                    double predictionError = 0.0;
                    // go through all the data
                    for (int j = 0, n = data.Length - windowSize; j < n; j++)
                    {
                        // put values from current window as variables
                        for (int k = 0, b = j + windowSize - 1; k < windowSize; k++)
                        {
                            input[k] = data[b - k];
                        }

                        // evalue the function
                        solution[j, 1] = PolishExpression.Evaluate(bestFunction, input);

                        // calculate prediction error
                        if (j >= n - predictionSize)
                        {
                            predictionError += Math.Abs(solution[j, 1] - data[windowSize + j]);
                        }
                        else
                        {
                            learningError += Math.Abs(solution[j, 1] - data[windowSize + j]);
                        }
                    }
                    // update solution on the chart
                    chart.UpdateDataSeries("solution", solution);

                    // set current iteration's info
                    SetText(currentIterationBox, i.ToString());
                    SetText(currentLearningErrorBox, learningError.ToString("F3"));
                    SetText(currentPredictionErrorBox, predictionError.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());
            for (int j = windowSize, k = 0, n = data.Length; j < n; j++, k++)
            {
                AddSubItem(dataList, j, solution[k, 1].ToString());
            }

            // enable settings controls
            EnableControls(true);
        }