Memetic Algorithms

Carlos Cotta (), Luke Mathieson () and Pablo Moscato ()
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Carlos Cotta: Universidad de Málaga, Instituto de Ingeniería y Tecnología del Software (ITIS)
Luke Mathieson: University of Technology Sydney, School of Computer Science
Pablo Moscato: University of Newcastle, School of Information and Physical Sciences

Chapter 27 in Handbook of Heuristics, 2025, pp 809-850 from Springer

Abstract: Abstract Memetic algorithms (MAs) provide a very effective and flexible metaheuristic approach for tackling hard optimization problems. MAs 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. In this chapter, we discuss the philosophy of the memetic paradigm, lay out the structure of an MA, develop several example algorithms, survey recent work in the field, and discuss the possible future directions of MAs.

Keywords: Evolutionary algorithms; Hybridization; Local search; Memetic computing; Metaheuristics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00385-0_29

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