Motivation
Memetic Algorithms have proven to be potent optimization frameworks which are capable of handling a wide range of problems. Stemming from the long-standing understating in the optimization community that no single algorithm can effectively accomplish global optimization [940], memetic algorithms combine global and local search components to balance exploration and exploitation [368, 765]: the global search explores the function landscape while the local search refines solutions. In literature the terms memetic algorithms [615, 673] and hybrid algorithms [325] refer to the same global–local framework just described. The merits of memetic algorithms have been demonstrated in numerous publications, [374, 375, 686, 688].
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© 2012 Springer-Verlag Berlin Heidelberg
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Tenne, Y. (2012). Memetic Algorithms in the Presence of Uncertainties. In: Neri, F., Cotta, C., Moscato, P. (eds) Handbook of Memetic Algorithms. Studies in Computational Intelligence, vol 379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23247-3_14
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DOI: https://doi.org/10.1007/978-3-642-23247-3_14
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