ABSTRACT
A partly time and space linear CMA-ES is benchmarked on the BBOB-2009 noisy function testbed. This algorithm with a multistart strategy with increasing population size solves 10 functions out of 30 in 20-D.
- ]]A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population size. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), pages 1769--1776. IEEE Press, 2005.Google ScholarCross Ref
- ]]S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noisy functions. Technical Report 2009/21, Research Center PPE, 2009.Google Scholar
- ]]N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.Google Scholar
- ]]N. Hansen, S. Finck, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Noisy functions definitions. Technical Report RR-6869, INRIA, 2009.Google Scholar
- ]]N. Hansen and A. Ostermeier. Completely derandomized self--adaptation in evolution strategies. Evolutionary computation, 9(2):159--195, 2001. Google ScholarDigital Library
- ]]M. Lunacek, D. Whitley, and A. Sutton. The impact of global structure on search. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, editors, PPSN, volume 5199 of Lecture Notes in Computer Science, pages 498--507. Springer, 2008.Google Scholar
- ]]R. Ros and N. Hansen. A simple modification in CMA-ES achieving linear time and space complexity. In G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, editors, Parallel Problem Solving from Nature -- PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings, volume 5199 of Lecture Notes in Computer Science, pages 296--305. Springer, 2008.Google Scholar
Index Terms
- Benchmarking sep-CMA-ES on the BBOB-2009 noisy testbed
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