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Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed
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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: 2467-2472  
Year of Publication: 2009
ISBN:978-1-60558-505-5
Authors
Anne Auger  INRIA Saclay Ile-de-France, Orsay, France
Nikolaus Hansen  Microsoft Research-INRIA Joint Centre, Orsay Cedex, France
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

We benchmark an independent-restart-(1+1)-CMA-ES on the BBOB-2009 noisy testbed. The (1+1)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions defined on a continuous search space in a black-box scenario. The maximum number of function evaluations used here equals 104 times the dimension of the search space. The algorithm could only solve $4$ functions with moderate noise in 5-D and 2 functions in 20-D.


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.

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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.
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N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA, 2009.
 
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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.
 
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Collaborative Colleagues:
Anne Auger: colleagues
Nikolaus Hansen: colleagues