Abstract
This paper presents a hybrid evolutionary optimization strategy combining the Mixed Bayesian Optimization Algorithm (MBOA) with variance adaptation as implemented in Evolution Strategies. This new approach is intended to circumvent some of the deficiences of MBOA with unimodal functions and to enhance its adaptivity. The Adaptive MBOA algorithm – AMBOA – is compared with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The comparison shows that, in continuous domains, AMBOA is more efficient than the original MBOA algorithm and its performance on separable unimodal functions is comparable to that of CMA-ES.
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Ocenasek, J., Kern, S., Hansen, N., Koumoutsakos, P. (2004). A Mixed Bayesian Optimization Algorithm with Variance Adaptation. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_36
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DOI: https://doi.org/10.1007/978-3-540-30217-9_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
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