A tutorial for competent memetic algorithms: Model, taxonomy and design issues

Krasnogor, N.; Smith, Jim

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.

• Journal Article Type: Article
• Publication Date: Oct 3, 2005
• Deposit Date: Aug 25, 2010
• Publicly Available Date: Mar 18, 2016
• Journal: IEEE Transactions on Evolutionary Computation
• Print ISSN: 1089-778X
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Not Peer Reviewed
• Volume: 9
• Issue: 5
• Pages: 474-488
• DOI: https://doi.org/10.1109/TEVC.2005.850260
• Keywords: competent memetic algorithms
• Public URL: https://uwe-repository.worktribe.com/output/1046934
• Publisher URL: http://dx.doi.org/10.1109/TEVC.2005.850260
• Additional Information: Additional Information : © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
• Contract Date: Mar 18, 2016