差分进化中集成策略回归方法的比较
Comparing various regression methods on ensemble strategies in differential evolution
摘要：
差分进化拥有大量的各种策略来产生新的试验解决方案。不幸的是，最好的策略是事先不知道的。而且，这种策略通常取决于要解决的问题。本文提出了在差分进化算法中使用各种回归方法（如随机森林、极端随机树、梯度提升、决策树和广义线性模型），通过预测运行过程中的最佳差分演化策略，对集成策略进行优化。通过优化一套五个著名的功能，从文献中比较该算法的初步结果，它表明，使用随机森林回归方法大大优于其他回归方法的结果。
英文摘要：
Differential evolution possesses a multitude of various strategies for generating new trial solutions. Unfortunately, the best strategy is not known in advance. Moreover, this strategy usually depends on the problem to be solved. This paper suggests using various regression methods (like random forest, extremely randomized trees, gradient boosting, decision trees, and a generalized linear model) on ensemble strategies in differential evolution algorithm by predicting the best differential evolution strategy during the run. Comparing the preliminary results of this algorithm by optimizing a suite of five well-known functions from literature, it was shown that using the random forest regression method substantially outperformed the results of the other regression methods.
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。
