ABSTRACT
The population size adaptation mechanism for CMA-ES is evaluated on the BBOB noiseless testbed. The population size is adapted on the basis of the estimated accuracy of the update of the distribution parameters, i.e., the mean vector and the covariance matrix of the Gaussian distribution. The population size is adapted so that the estimated accuracy of the parameter update keeps a certain level. The CMA- ES with the population size adaptation mechanism could solve well-structured multimodal functions as efficiently as the best 2009 portfolio without a restart strategy that in- creases the population size every restart such as the IPOP strategy.
- A. Ahrari and M. Shariat-Panahi. An improved evolution strategy with adaptive population size. Optimization, 64(12):2567--2586, 2015.Google Scholar
- Y. Akimoto, Y. Nagata, I. Ono, and S. Kobayashi. Bidirectional relation between CMA evolution strategies and natural evolution strategies. In Parallel Problem Solving from Nature -- PPSN XI, pages 154--163, Springer-Verlag, 2010. Google ScholarDigital Library
- 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. Updated February 2010.Google Scholar
- N. Hansen. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In Workshop Proceedings of the Genetic and Evolutionary Computation Conference, pages 2389--2395, ACM, 2009. Google ScholarDigital Library
- N. Hansen, A. Atamna, and A. Auger. How to assess step-size adaptation mechanisms in randomised search. In Parallel Problem Solving from Nature--PPSN XIII, pages 60--69. Springer, 2014.Google Scholar
- N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2012: Experimental setup. Technical report, INRIA, 2012.Google Scholar
- 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. Updated February 2010.Google Scholar
- N. Hansen and S. Kern. Evaluating the CMA evolution strategy on multimodal test functions. In Parallel Problem Solving from Nature -- PPSN VIII, pages 282--291. Springer, 2004.Google Scholar
- N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, 2001. Google ScholarDigital Library
- K. Nishida and Y. Akimoto. Population size adaptation for the CMA-ES based on the estimation accuracy of the natural gradient. In Proceedings of the Genetic and Evolutionary Computation Conference, ACM, 2016. To appear. Google ScholarDigital Library
- Y. Ollivier, L. Arnold, A. Auger, and N. Hansen. Information-geometric optimization algorithms: A unifying picture via invariance principles. Preprint arXiv:1106.3708.Google Scholar
- K. Price. Differential evolution vs. the functions of the second ICEO. In Proceedings of the IEEE International Congress on Evolutionary Computation, pages 153--157, 1997.Google Scholar
Index Terms
- Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noiseless Testbed
Recommendations
Evaluating the Population Size Adaptation Mechanism for CMA-ES on the BBOB Noisy Testbed
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference CompanionThe CMA-ES with a population size adaptation mechanism is benchmarked on the BBOB noisy testbed. The population size is adapted online based on the signal-to-noise ratio of the update of the distribution parameters such as the mean vector and the ...
Comparison of cauchy EDA and BIPOP-CMA-ES algorithms on the BBOB noiseless testbed
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computationEstimation-of-distribution algorithm using Cauchy sampling distribution is compared with the bi-population CMA evolutionary strategy which was one of the best contenders in the black-box optimization benchmarking workshop in 2009. The results clearly ...
Benchmarking the PSA-CMA-ES on the BBOB noiseless testbed
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference CompanionWe evaluate the CMA-ES with population size adaptation mechanism (PSA-CMA-ES) on the BBOB noiseless testbed. On one hand, the PSA-CMA-ES with a simple restart strategy shows performance competitive with the best 2009 portfolio on most well-structured ...
Comments