基于Q学习的自适应结构超参数配置
Adaptive Structural Hyper-Parameter Configuration by Q-Learning
摘要：
进化算法的超参数调整是计算智能领域的一个重要问题。进化算法的性能不仅取决于其运行策略的设计，而且还取决于其超参数。超参数可以分为结构/数值和时不变/时变两个维度。特别是，在现有的研究中，结构超参数通常是预先调整的时不变参数，或与手工制作的调度时不变参数。本文首次尝试将结构超参数的调整建模为一个强化学习问题，并提出通过Q学习来调整CEC 2018 winner算法中控制计算资源分配的结构超参数。在CEC 2018测试函数上的实验结果表明，该算法优于优胜者算法。
英文摘要：
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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