AForge.Neuro.Learning.EvolutionaryFitness.Evaluate C# (CSharp) Method

        public double Evaluate( IChromosome chromosome )
        {
            DoubleArrayChromosome daChromosome = (DoubleArrayChromosome) chromosome;
            double[] chromosomeGenes = daChromosome.Value;
            // total number of weight in neural network
            int totalNumberOfWeights = 0;

            // asign new weights and thresholds to network from the given chromosome
            for ( int i = 0, layersCount = network.Layers.Length; i < layersCount; i++ )
            {
                Layer layer = network.Layers[i];

                for ( int j = 0; j < layer.Neurons.Length; j++ )
                {
                    ActivationNeuron neuron = layer.Neurons[j] as ActivationNeuron;

                    for ( int k = 0; k < neuron.Weights.Length; k++ )
                    {
                        neuron.Weights[k] = chromosomeGenes[totalNumberOfWeights++];
                    }
                    neuron.Threshold = chromosomeGenes[totalNumberOfWeights++];
                }
            }

            // post check if all values are processed and lenght of chromosome
            // is equal to network size
            Debug.Assert( totalNumberOfWeights == daChromosome.Length );

            double totalError = 0;

            for ( int i = 0, inputVectorsAmount = input.Length; i < inputVectorsAmount; i++ )
            {
                double[] computedOutput = network.Compute( input[i] );

                for ( int j = 0, outputLength = output[0].Length; j < outputLength; j++ )
                {
                    double error = output[i][j] - computedOutput[j];
                    totalError += error * error;
                }
            }

            if ( totalError > 0 )
                return 1.0 / totalError;

            // zero error means the best fitness
            return double.MaxValue;
        }
    }