Fitness Function Approximation by Neural Networks in the Optimization of MGP-FIR Filters

In this paper we introduce a neural network based method for speeding up the fitness function calculations in a genetic algorithm (GA)-driven optimization process of multiplicative general parameter finite impulse response (MGP-FIR) filters. In this case, calculating the fitness of a candidate solution is an extensive and time-consuming task. However, our results show that it is possible to approximate the fitness function components with neural networks up to sufficient degree, thus enabling the genetic algorithm to perform the fitness calculations considerably faster. This allows the algorithm to evaluate larger number of generations in a given time. Our results suggest that it is possible to decrease the approximation error of the neural network so that the NN-assisted GA eventually offers competitive performance compared to a reference GA