Memetic Algorithms

Carlos Cotta (), Luke Mathieson () and Pablo Moscato ()
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Carlos Cotta: Universidad de Málaga, Departamento Lenguajes y Ciencias de la Computación
Luke Mathieson: University of Newcastle, Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine
Pablo Moscato: The University of Newcastle, School of Electrical Engineering and Computing, Faculty of Engineering and Built Environment

Chapter 20 in Handbook of Heuristics, 2018, pp 607-638 from Springer

Abstract: Abstract Memetic algorithms provide one of the most effective and flexible metaheuristic approaches for tackling hard optimization problems. Memetic algorithms address the difficulty of developing high-performance universal heuristics by encouraging the exploitation of multiple heuristics acting in concert, making use of all available sources of information for a problem. This approach has resulted in a rich arsenal of heuristic algorithms and metaheuristic frameworks for many problems. This chapter discusses the philosophy of the memetic paradigm, lays out the structure of a memetic algorithm, develops several example algorithms, surveys recent work in the field, and discusses the possible future directions of memetic algorithms.

Keywords: Evolutionary algorithms; Hybridization; Local search; Memetic computing; Metaheuristics (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-07124-4_29

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