Investigation of Distributed and Parallel Performance of a Genetic Algorithm

Abstract

Genetic algorithms, a stochastic evolutionary computing technique, have demonstrated a capacity for robust, efficient problem solving through highly parallel search space exploration. This work demonstrates how an improvement in performance and efficiency over the traditional serial approach can be achieved by exploiting this highly parallel nature to produce parallel genetic algorithms. Furthermore, it is shown that by incorporating domain specific knowledge into a genetic algorithm near optimal solutions can be located in minimal time.