论文标题
Q学习的自适应结构超参数配置
Adaptive Structural Hyper-Parameter Configuration by Q-Learning
论文作者
论文摘要
调整进化算法的超参数是计算智能中的重要问题。进化算法的性能不仅取决于其操作策略设计，还取决于其超参数。超参数可以在两个维度上归类为结构/数值和时变/时间变化。特别是，在现有研究中，结构性超参数通常会提前调整为时间不变的参数，或者以手工制作的时间表进行时间不变的参数进行调整。在本文中，我们首次尝试将结构超参数调整为增强学习问题进行建模，并提出调整结构性超参数，该参数控制CEC 2018获奖者算法中通过Q-LEARNING控制计算资源分配。实验结果对CEC 2018测试功能的获胜算法表现出了有利的表现。
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.
