A Modern 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 6 in Handbook of Metaheuristics, 2010, pp 141-183 from Springer

Abstract: Abstract Memetic algorithms are optimization techniques based on the synergistic combination of ideas taken from different algorithmic solvers, such as population-based search (as in evolutionary techniques) and local search (as in gradient-ascent techniques). After providing some historical notes on the origins of memetic algorithms, this work shows the general structure of these techniques, including some guidelines for their design. Some advanced topics such as multiobjective optimization, self-adaptation, and hybridization with complete techniques (e.g., branch-and-bound) are subsequently addressed. This chapter finishes with an overview of the numerous applications of these techniques and a sketch of the current development trends in this area.

Keywords: Local Search; Combinatorial Optimization Problem; Vertex Cover; Memetic Algorithm; Fitness Landscape (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_6

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