利用策略梯度学习优化经验的自适应差分进化算法
Learning adaptive differential evolution algorithm from optimization experiences by policy gradient
摘要：
差分进化算法是求解黑箱问题的一种基于种群的随机优化算法。差分进化算法的性能在很大程度上取决于其变异和交叉策略以及相关的控制参数。然而，最合适的参数设置的确定过程是麻烦且耗时的。自适应控制参数的方法，可以适应问题的景观和优化环境比固定的参数设置更可取。本文提出了一种新的自适应参数控制方法的基础上学习的优化经验的一组问题。在该方法中，参数控制建模为有限时域马尔可夫决策过程。一种强化学习算法，命名为策略梯度，学习一个代理（即参数控制器），可以提供建议的差分进化的控制参数自适应搜索过程中。在CEC'13和CEC'17测试集上，将基于学习代理的差分进化算法与9种著名的进化算法进行了比较。实验结果表明，该算法在测试集上的性能优于这些比较算法。
英文摘要：
Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination process for the most suitable parameter setting is troublesome and time-consuming. Adaptive control parameter methods that can adapt to problem landscape and optimization environment are more preferable than fixed parameter settings. This paper proposes a novel adaptive parameter control approach based on learning from the optimization experiences over a set of problems. In the approach, the parameter control is modeled as a finite-horizon Markov decision process. A reinforcement learning algorithm, named policy gradient, is applied to learn an agent (i.e. parameter controller) that can provide the control parameters of a proposed differential evolution adaptively during the search procedure. The differential evolution algorithm based on the learned agent is compared against nine well-known evolutionary algorithms on the CEC'13 and CEC'17 test suites. Experimental results show that the proposed algorithm performs competitively against these compared algorithms on the test suites.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
