On the impact of objective function transformations on evolutionary and black-box algorithms

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On the impact of objective function transformations on evolutionary and black-box algorithms

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 2005 conference on Genetic and evolutionary computation table of contents

Washington DC, USA

SESSION: Evolutionary strategies and evolutionary programming table of contents

Pages: 833 - 840

Year of Publication: 2005

ISBN:1-59593-010-8

Author

Tobias Storch

University of Dortmund, Dortmund, Germany

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 2, Downloads (12 Months): 18, Citation Count: 2

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ABSTRACT

Different fitness functions describe different problems. Hence, certain fitness transformations can lead to easier problems although they are still a model of the considered problem. In this paper, the class of neutral transformations for a simple rank-based evolutionary algorithm (EA) is described completely, i.e., the class of functions that transfers easy problems for this EA in easy ones and difficult problems in difficult ones. Moreover, the class of neutral transformations for this population-based EA is equal to the black-box neutral transformations. Hence, it is a proper superset of the corresponding class for an EA based on fitness-proportional selection, but it is a proper subset of the class for random search. Furthermore, the minimal and maximal classes of neutral transformations are investigated in detail.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Thomas Back , David B. Fogel , Zbigniew Michalewicz, Handbook of Evolutionary Computation, IOP Publishing Ltd., Bristol, UK, 1997
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	3	S. Droste, T. Jansen, and I. Wegener. Upper and lower bounds for randomized search heuristics in black-box optimization. Theory of Computing Systems, 2005. Accepted for publication.
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CITED BY 2

	Ingo Mierswa, Controlling overfitting with multi-objective support vector machines, Proceedings of the 9th annual conference on Genetic and evolutionary computation, July 07-11, 2007, London, England
	Ingo Mierswa, Evolutionary learning with kernels: a generic solution for large margin problems, Proceedings of the 8th annual conference on Genetic and evolutionary computation, July 08-12, 2006, Seattle, Washington, USA