Choice of Memes In Memetic Algorithm

Abstract

One of the fastest growing areas of evolutionary algorithm research is the enhancement of genetic algorithms by combination with local search methods or memes: often known otherwise as memetic algorithms. However there is often little theoretical basis on which to characterize the choice of memes that lead to successful memetic algorithm performance. In this paper, we investigate empirically the use of different memes in the memetic algorithms across a variety of benchmark test functions for function optimization. Empirical results show that the choice of memes affects the search performance significantly. Further, an investigation on the random choice of memes at each decision point during the memetic algorithm search arrives at interesting results.