Abstract
We focus on a variant of covariance matrix adaptation evolution strategy (CMA-ES) with a restricted covariance matrix model, namely VkD-CMA, which is aimed at reducing the internal time complexity and the adaptation time in terms of function evaluations. We tackle the shortage of the VkD-CMA—the model of the restricted covariance matrices needs to be selected beforehand. We propose a novel mechanism to adapt the model online in the VkD-CMA. It eliminates the need for advance model selection and leads to a performance competitive with or even better than the algorithm with a nearly optimal but fixed model.
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Notes
- 1.
The default \(c_1\) is slightly different from the original setting in [3]. The value presented in the paper is slightly more stable for k close to zero.
- 2.
If we use only nonnegative weights as we do in this paper, the possible convergence rate halves.
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This work is partially supported by JSPS KAKENHI Grant Number 15K16063.
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Akimoto, Y., Hansen, N. (2016). Online Model Selection for Restricted Covariance Matrix Adaptation. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_1
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