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Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2389 - 2396

https://doi.org/10.1145/1570256.1570333

Published: 08 July 2009 Publication History

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Abstract

We propose a multistart CMA-ES with equal budgets for two interlaced restart strategies, one with an increasing population size and one with varying small population sizes. This BI-population CMA-ES is benchmarked on the BBOB-2009 noiseless function testbed and could solve 23, 22 and 20 functions out of 24 in search space dimensions 10, 20 and 40, respectively, within a budget of less than $10^6 D$ function evaluations per trial.

References

[1]

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.

Crossref

Google Scholar

[2]

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.

Google Scholar

[3]

N. Hansen. The CMA evolution strategy: a comparing review. In J. Lozano, P. Larranaga, I. Inza, and E. Bengoetxea, editors, Towards a new evolutionary computation. Advances on estimation of distribution algorithms, pages 75--102. Springer, 2006.

Google Scholar

[4]

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

[5]

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.

Google Scholar

[6]

N. Hansen and S. Kern. Evaluating the CMA evolution strategy on multimodal test functions. In X. Yao et al., editors, Parallel Problem Solving from Nature -- PPSN VIII, LNCS 3242, pages 282--291. Springer, 2004.

Google Scholar

[7]

N. Hansen, S. D. Müller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11(1):1--18, 2003.

Digital Library

Google Scholar

[8]

N. Hansen, A. Niederberger, L. Guzzella, and P. Koumoutsakos. A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion. IEEE Transactions on Evolutionary Computation, 13(1):180--197, 2009.

Digital Library

Google Scholar

[9]

N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, 2001.

Digital Library

Google Scholar

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Index Terms

Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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https://doi.org/10.1016/j.apm.2025.115965
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Recommendations

Bi-population CMA-ES agorithms with surrogate models and line searches

Benchmarking a BI-population CMA-ES on the BBOB-2009 noisy testbed

Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed

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