The (1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate

We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mu...

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Veröffentlicht in:	Algorithmica Jg. 81; H. 2; S. 593 - 631
Hauptverfasser:	Doerr, Benjamin, Gießen, Christian, Witt, Carsten, Yang, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 15.02.2019 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithm Analysis and Problem Complexity Algorithms Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Evolutionary algorithms Fitness Genetic algorithms Mathematics of Computing Mutation Optimization Parameters Special Issue on Theory of Genetic and Evolutionary Computation Theory of Computation Yeast Mutation Runtime analysis Self-adaptation Evolutionary computation
ISSN:	0178-4617, 1432-0541
Online-Zugang:	Volltext
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