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

By Natalio Krasnogor, James Smith. IEEE Transactions on Evolutionary Computation, 2005

777 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/ef505ffc246067a627ff0c01e9d16b99593c500b

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Citations in the corpus

Hybrid metaheuristics in combinatorial optimization: A survey (2011, background): “Evolutionary algorithms making use of local search methods are sometimes labelled as memetic algorithms [29, 30].”
A survey of search methodologies and automated system development for examination timetabling (2009, background): “Memetic algorithms [116] are an extension of Genetic Algorithms whose basic idea is that individuals in a population can be improved during their lifetime (i.”
From evolutionary computation to the evolution of things (2015, background): “Such hybrid algorithms can often find good (or better) solutions faster than a pure EA when the additional method searches systematically in the vicinity of good solutions, rather than relying on the more randomised search carried out by mutation 46,47 .”
A Multi-Facet Survey on Memetic Computation (2011, background): “In particular, it is now wellestablished [60] that potential algorithmic improvements can be achieved by adapting several key designs, including the frequency of refinement, selection of individual subset to”
Memetic algorithms and memetic computing optimization: A literature review (2012, background): “, [105, 199, 230], where the memes, either directly encoded within the candidate solutions or evolving in parallel to them, take part in the evolution and undergo recombination and selection in order to select the most promising operators”
Hybrid Genetic Algorithms: A Review (2006, background): “However, the reader can refer to [18] for an architectural taxonomy of combinatorial memetic algorithms (MA) [19] and to [20], where meta-heuristics are classified based on the design space and implantation space aspects.”
Differential Evolution Using a Neighborhood-Based Mutation Operator (2009, background): “3) MA-S2: Memetic algorithms (MAs) [63], [ 64 ] are based on the hybridization of genetic algorithm (GA) with local search (LS) techniques.”