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arXiv · 2003.00863

Adaptive Structural Hyper-Parameter Configuration by Q-Learning

Haotian Zhang·Jianyong Sun·Zongben Xu
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Abstract

Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence. Performance of an evolutionary algorithm depends not only on its operation strategy design, but also on its hyper-parameters. Hyper-parameters can be categorized in two dimensions as structural/numerical and time-invariant/time-variant. Particularly, structural hyper-parameters in existing studies are usually tuned in advance for time-invariant parameters, or with hand-crafted scheduling for time-invariant parameters. In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the structural hyper-parameter which controls computational resource allocation in the CEC 2018 winner algorithm by Q-learning. Experimental results show favorably against the winner algorithm on the CEC 2018 test functions.

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Haotian Zhang, Jianyong Sun, Zongben Xu. 2020-03-02. Adaptive Structural Hyper-Parameter Configuration by Q-Learning. https://doi.org/10.1109/cec48606.2020.9185665

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