论文标题
我们距离最佳，自适应DE有多远？
How Far Are We From an Optimal, Adaptive DE?
论文作者
论文摘要
我们考虑自适应DE的（几乎）最佳参数适应过程可能会表现出来，并将这种近似最佳过程的行为和性能与现有的自适应机制的行为和性能进行比较。最佳参数适应过程是一个有用的概念，用于分析自适应DE以及其他自适应进化算法中的参数适应方法，但一般不知道。因此，我们提出了一个贪婪的近似甲骨文方法（GAO），该方法近似于最佳参数适应过程。我们将GAODE（DE Algorithm与GAO）的行为与六个基准功能和BBOB基准测试的典型自适应进行了比较，并表明GAO可以用来（1）探索适应性DES的表现中有多少改进空间，并且（2）获得有效的参数适应性的提示，以供有效的参数适应方法。
We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to that of existing, adaptive mechanisms for DE. An optimal parameter adaptation process is an useful notion for analyzing the parameter adaptation methods in adaptive DE as well as other adaptive evolutionary algorithms, but it cannot be known generally. Thus, we propose a Greedy Approximate Oracle method (GAO) which approximates an optimal parameter adaptation process. We compare the behavior of GAODE, a DE algorithm with GAO, to typical adaptive DEs on six benchmark functions and the BBOB benchmarks, and show that GAO can be used to (1) explore how much room for improvement there is in the performance of the adaptive DEs, and (2) obtain hints for developing future, effective parameter adaptation methods for adaptive DEs.
