Memetic algorithms in the presence of uncertainties

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations


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].

Original languageEnglish
Title of host publicationHandbook of Memetic Algorithms
EditorsFerrante Neri, Carlos Cotta, Pablo Moscato
Number of pages19
StatePublished - 2012
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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