Doubly Trained Evolution Control for the Surrogate CMA-ES
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This paper presents a new variant of surrogate-model utilization in expensive continuous evolutionary black-box optimization. This algorithm is based on the surrogate version of the CMA-ES, the Surrogate Covariance Matrix Adaptation Evolution Strategy (S-CMA-ES). Similarly to the original S-CMA-ES, expensive function evaluations are saved through a surrogate model. However, the model is retrained after the points in which its prediction was most uncertain have been evaluated by the true fitness in each generation. We demonstrate that within small budget of evaluations, the new variant of S-CMA-ES improves the original algorithm and outperforms two state-of-the-art surrogate optimizers, except a few evaluations at the beginning of the optimization process.
KeywordsBlack-box optimization Surrogate model Evolution control Gaussian process
This work was supported by the Grant Agency of the Czech Technical University in Prague with its grant No. SGS14/205/OHK4/3T/14 by the Czech Health Research Council project NV15-33250A, by the project “National Institute of Mental Health (NIMH-CZ)”, grant number ED2.1.00/03.0078 and the European Regional Development Fund, and by the project Nr.LO1611 with a financial support from the MEYS under the NPU I program. Further, access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the programme “Projects of Large Infrastructure for Research, Development, and Innovations” (LM2010005), is greatly appreciated.
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