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Speeding up Evolutionary Algorithms Through Asymmetric Mutation Operators

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Doerr,  Benjamin
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Hebbinghaus,  Nils
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Neumann,  Frank
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Doerr, B., Hebbinghaus, N., & Neumann, F. (2007). Speeding up Evolutionary Algorithms Through Asymmetric Mutation Operators. Evolutionary Computation, 15(4), 401-410. doi:10.1162/evco.2007.15.4.401.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-20C3-9
Abstract
Successful applications of evolutionary algorithms show that certain variation operators can lead to good solutions much faster than other ones. We examine this behavior observed in practice from a theoretical point of view and investigate the effect of an asymmetric mutation operator in evolutionary algorithms with respect to the runtime behavior. Considering the Eulerian cycle problem we present runtime bounds for evolutionary algorithms using an asymmetric operator which are much smaller than the best upper bounds for a more general one. In our analysis it turns out that a plateau which both algorithms have to cope with changes its structure in a way that allows the algorithm to obtain an improvement much faster. In addition, we present a lower bound for the general case which shows that the asymmetric operator speeds up computation by at least a linear factor.