Competent Memetic Algorithms: Model, Taxonomy and Dessing Issues

Evolutionary algorithms combined with local search were named “Memetic Algorithms” (MAs) in [1]. These methods are inspired by models of adaptation in natural systems that combine evolutionary adaptation of populations of individuals with individual learning within a lifetime. Additionally, MAs are inspired by Richard Dawkins concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement[2]. In the case of MAs “memes” refer to the strategies (e.g. local refinement, perturbation or constructive methods, etc) individuals (also called “agents”) employ to improve themselfs. In this paper we will review some works on the application of MAs to well known combinatorial optimization problems. A syntactic model will be defined and a classification scheme based on a computable index will be given. The existence of both a model and a taxonomy for MAs is of theoretical and practical relevance as it aids in conceptualizing important desing issues that must be address if one intend to produce competent Memetic Algorithms. Also, by having an abstract model for this metaheuristics it is possible to explore their design space and better understand their behavior from a theoretical standpoint.