An Accelerated Introduction to Memetic Algorithms

Pablo Moscato () and Carlos Cotta ()
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Pablo Moscato: The University of Newcastle
Carlos Cotta: Universidad de Málaga

Chapter Chapter 9 in Handbook of Metaheuristics, 2019, pp 275-309 from Springer

Abstract: Abstract Memetic algorithms (MAs) are optimization techniques based on the orchestrated interplay between global and local search components and have the exploitation of specific problem knowledge as one of their guiding principles. In its most classical form, a MA is typically composed of an underlying population-based engine onto which a local search component is integrated. These aspects are described in this chapter in some detail, paying particular attention to design and integration issues. After this description of the basic architecture of MAs, we move to different algorithmic extensions that give rise to more sophisticated memetic approaches. After providing a meta-review of the numerous practical applications of MAs, we close this chapter with an overview of current perspectives of memetic algorithms.

Keywords: Memetic Algorithm (MAs); Local Search Component; Greedy Randomized Adaptive Search Procedure (GRASP); Newpop; Multiple Knapsack Problem (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-319-91086-4_9

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