Training Feed-Forward Neural Networks Using a Parallel Genetic Algorithm with the Best Must Survive Strategy

Feed-forward artificial neural networks (FFANN) can be trained using genetic algorithm (GA). GA offers a stochastic global optimization technique that might suffer from two major shortcomings: slow convergence time and impractical data representation. The effect of these shortcomings is more considerable in case of larger FFANN with larger dataset. Using a non-binary real-coded data representation we offer an enhancement to the generational GA used for the training of FFANN. Such enhancement would come in two fold: The first being a new strategy to process the strings of the population by allowing the fittest string to survive unchanged to the next population depending on its age. The second is to speed up fitness computation time through the utilization of known parallel processing techniques used for matrix multiplication. The implementation was carried on master-slaves architecture of commodity computers connected via Ethernet. Using a well-known benchmarking dataset, results show that our proposed technique is superior to the standard in terms of both the overall convergence time and processing time.