Abstract
One of the most important issues for evolutionary computation (EC) is to consider the number of fitness evaluations. In order to reduce the number of fitness evaluations, we have proposed the novel surrogate model called Rank Space Estimation (RSE) model and the surrogate-assisted EC with RSE model called the Fitness Landscape Learning Evolutionary Computation (FLLEC). This paper presents a novel CMA-ES with RSE model for continuous optimization problems and a scaling method for input data to surrogate model.
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Acknowledgments
A part of this work was supported by JSPS KAKENHI Grant, Grant-in-Aid for Scientific Research(C), 26330282, by JSPS KAKENHI Grant, Grant-in-Aid for JSPS Fellows, 16J10941, and by Program for Leading Graduate Schools of Ministry of Education, Culture, Sports, Science and Technology in Japan. We are grateful to Dr. Ilya Loshchilov for helpful discussions.
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Tsukada, K., Hasegawa, T., Mori, N., Matsumoto, K. (2017). CMA-ES with Surrogate Model Adapting to Fitness Landscape. In: Leu, G., Singh, H., Elsayed, S. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-49049-6_30
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DOI: https://doi.org/10.1007/978-3-319-49049-6_30
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