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BBOB: Nelder-Mead with resize and halfruns

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers table of contents

Montreal, Québec, Canada

WORKSHOP SESSION: Black box optimization benchmarking (BBOB) table of contents

Pages 2239-2246

Year of Publication: 2009

ISBN:978-1-60558-505-5

Authors

Benjamin Doerr	Max-Planck-Institut fur Informatik, Saarbrucken, Germany
Mahmoud Fouz	Max-Planck-Institut fur Informatik, Saarbrucken, Germany
Martin Schmidt	Universitat des Saarlandes, Saarbrucken, Germany
Magnus Wahlstrom	Max-Planck-Institut fur Informatik, Saarbrucken, 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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ABSTRACT

Using the BBOB template, we investigate how the Nelder-Mead simplex algorithm can be combined with evolutionary ideas to give a competitive hybrid approach to optimize continuous functions. We significantly improve the performance of the algorithm in higher dimension by the addition of a reshaping step of the search, to correct for a known problem in the simplex search behaviour. We also give a reasonably good population-based approach in which only a third of the individuals is fully matured, with a bias towards fitter individuals, via a variant of the Nelder-Mead method.

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	S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE, 2009.
	2	N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.
	3	N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions. Technical Report RR-6829, INRIA, 2009.
	4	J. A. Nelder and R. Mead. A Simplex Method for Function Minimization. The Computer Journal, 7(4):308--313, 1965.