A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues

TLDR

Memetic algorithms combine evolutionary search with local refinement, drawing on natural system models and Dawkins’ meme concept to guide individual learning and strategy improvement. This paper reviews applications of memetic algorithms to classic combinatorial optimization problems and situates them within a general syntactic framework. The framework introduces a computable index D that classifies algorithms, enabling comparative analysis and exploration of the design space from a theoretical perspective. The authors demonstrate the model’s theoretical and practical value by highlighting key design issues essential for developing effective and efficient memetic algorithms.

Abstract

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.