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Making EGO and CMA-ES Complementary for Global Optimization

Abstract : The global optimization of expensive-to-calculate continuous functions is of great practical importance in engineering. Among the proposed algorithms for solving such problems, Efficient Global Optimization (EGO) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are regarded as two state-of-the-art unconstrained continuous optimization algorithms. Their underlying principles and performances are different, yet complementary: EGO fills the design space in an order controlled by a Gaussian process (GP) conditioned by the objective function while CMA-ES learns and samples multi-normal laws in the space of design variables. This paper proposes a new algorithm, called EGO-CMA, which combines EGO and CMA-ES. In EGO-CMA, the EGO search is interrupted early and followed by a CMA-ES search whose starting point, initial step size and covariance matrix are calculated from the already sampled points and the associated conditional GP. EGO-CMA improves the performance of both EGO and CMA-ES in our 2 to 10 dimensional experiments.
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Contributor : Florent Breuil <>
Submitted on : Friday, June 26, 2015 - 9:02:34 AM
Last modification on : Wednesday, February 24, 2021 - 4:24:03 PM



Hossein Mohammadi, Rodolphe Le Riche, Eric Touboul. Making EGO and CMA-ES Complementary for Global Optimization. Learning and Intelligent Optimization, Volume 8994, pp 287-292, 2015, 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers, ⟨10.1007/978-3-319-19084-6_29⟩. ⟨emse-01168512⟩



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