Memetic Algorithms: Parametrization and Balancing Local and Global Search

This chapter is devoted to the parametrization of memetic algorithms and how to find a good balance between global and local search. This is one of the most pressing questions when designing a hybrid algorithm. The idea of hybridization is to combine the advantages of different components. But if one components dominates another one, hybridization may become more hindering than useful and computational effort may be wasted. For the case of memetic algorithms, if the effect of local search is too strong, the algorithm may quickly get stuck in local optima of bad quality. Moreover, the algorithm is likely to rediscover the same local optimum over and over again. Lastly, too much local search quickly leads to a loss of diversity within the population. The importance of the parametrization of memetic algorithms has already been recognized by Hart [10] in 1994. He posed the following questions, many of which have been reproduced in similar ways in later articles: