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Destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming

  • For theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation: the variable size representations, in particular yielding a possible bloat (i.e. the growth of individuals with redundant parts); and also the role and the realization of crossover, which is particularly central in GP due to the tree-based representation. Whereas some theoretical work on GP has studied the effects of bloat, crossover had surprisingly little share in this work. <br /> We analyze a simple crossover operator in combination with randomized local search, where a preference for small solutions minimizes bloat (lexicographic parsimony pressure); we denote the resulting algorithm Concatenation Crossover GP. We consider three variants of the well-studied MAJORITY test function, adding large plateaus in different ways to the fitness landscape and thus giving a test bed for analyzing the interplay of variation operators and bloat control mechanisms in a setting with local optima. We show that the ConcatenationFor theoretical analyses there are two specifics distinguishing GP from many other areas of evolutionary computation: the variable size representations, in particular yielding a possible bloat (i.e. the growth of individuals with redundant parts); and also the role and the realization of crossover, which is particularly central in GP due to the tree-based representation. Whereas some theoretical work on GP has studied the effects of bloat, crossover had surprisingly little share in this work. <br /> We analyze a simple crossover operator in combination with randomized local search, where a preference for small solutions minimizes bloat (lexicographic parsimony pressure); we denote the resulting algorithm Concatenation Crossover GP. We consider three variants of the well-studied MAJORITY test function, adding large plateaus in different ways to the fitness landscape and thus giving a test bed for analyzing the interplay of variation operators and bloat control mechanisms in a setting with local optima. We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control. (C) 2019 Elsevier B.V. All rights reserved.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Timo KötzingORCiD, Gregor J. A. LagodzinskiORCiD, Johannes LenglerORCiD, Anna MelnichenkoORCiDGND
DOI:https://doi.org/10.1016/j.tcs.2019.11.036
ISSN:0304-3975
Titel des übergeordneten Werks (Englisch):Theoretical computer science
Verlag:Elsevier
Verlagsort:Amsterdam
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:06.05.2020
Erscheinungsjahr:2020
Datum der Freischaltung:30.03.2023
Freies Schlagwort / Tag:genetic programming; mutation; run time analysis; theory
Band:816
Seitenanzahl:18
Erste Seite:96
Letzte Seite:113
Fördernde Institution:COST ActionEuropean Cooperation in Science and Technology (COST); [CA15140]
Organisationseinheiten:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Peer Review:Referiert
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