Memetic and hybrid evolutionary algorithms

Abstract

This chapter presents an overview of hybridization mechanisms in evolutionary algorithms. Such mechanisms are aimed to introducing problem knowledge in the optimization technique by means of the synergistic combination of general-purpose methods and problemspecific add-ons. This combination is presented in this work from two wide perspectives: memetic algorithms and cooperative optimization models. Memetic algorithms are based on the smart orchestration of global (population-based) and local (trajectorybased) techniques, using an algorithmic scheme in which the latter are often subordinated to the former. As to cooperative models, they are based on the collaboration of different optimization techniques that exchange information in order to boost their respective performances. Both approaches, memetic algorithms and cooperative models, provide a framework to achieve synergistic algorithmic combinations for the resolution of large-scale combinatorial problems.