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.
CITATION STYLE
Mohammadi, H., Le Riche, R., & Touboul, E. (2015). Making EGO and CMA-ES complementary for global optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8994, pp. 287–292). Springer Verlag. https://doi.org/10.1007/978-3-319-19084-6_29
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