Fitness function for times series prediction problem

The fitness function calculates fitness value of GP and GEP chromosomes with the aim of solving times series prediction problem using sliding window method. The fitness function's value is computed as: 100.0 / ( error + 1 ) where error equals to the sum of absolute differences between predicted value and actual future value.

Sample usage:

// number of points from the past used to predict new one int windowSize = 5; // time series to predict double[] data = new double[13] { 1, 2, 4, 7, 11, 16, 22, 29, 37, 46, 56, 67, 79 }; // constants double[] constants = new double[10] { 1, 2, 3, 5, 7, 11, 13, 17, 19, 23 }; // create population Population population = new Population( 100, new GPTreeChromosome( new SimpleGeneFunction( windowSize + constants.Length ) ), new TimeSeriesPredictionFitness( data, windowSize, 1, constants ), new EliteSelection( ) ); // run one epoch of the population population.RunEpoch( );
Inheritance: IFitnessFunction
TimeSeriesPredictionFitness Class Documentation
Beispiel #1
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		// 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 );
		}