Self-Adjusting Evolutionary Algorithms for Multimodal Optimization

Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functions which do not require the algorithm to flip sev...

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Veröffentlicht in:	Algorithmica Jg. 84; H. 6; S. 1694 - 1723
Hauptverfasser:	Rajabi, Amirhossein, Witt, Carsten
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2022 Springer Nature B.V
Schlagworte:	2020 Algorithm Analysis and Problem Complexity Algorithms Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Evolutionary algorithms Genetic algorithms Mathematics of Computing Modules Optimization Special Issue on Genetic and Evolutionary Computation Stagnation Theory of Computation Randomized search heuristics Self-adjusting algorithms Runtime analysis Multimodal functions
ISSN:	0178-4617, 1432-0541
Online-Zugang:	Volltext
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