停滞检测与快速变异相遇
Stagnation Detection Meets Fast Mutation
AI总结：
本文提出一种结合停滞检测与重尾快速变异的新变异策略，既能保持寻找单个远处解的最优概率，又在存在多个改进解时同时超越两种原有机制及其交错形式。
摘要：
最近提出了两种机制，它们能够通过变异显著加快找到远处的改进解，即使用从重尾分布中抽取的随机变异率（“快速变异”，Doerr et al. (2017)）以及基于停滞检测增加变异强度（Rajabi and Witt (2020)）。后者能够在给定距离内以渐近最优的概率找到单个期望解，而前者更鲁棒，并且当某个距离内存在许多改进解时表现要好得多。在这项工作中，我们提出了一种结合这两种机制思想的变异策略。我们证明它也能以最佳可能概率找到单个远处解。然而，当存在多个改进解时，它能够同时优于停滞检测方法和快速变异。这个新算子不仅仅是两种先前机制的简单交错，并且它也优于任何此类交错。
英文摘要：
Two mechanisms have recently been proposed that can significantly speed up finding distant improving solutions via mutation, namely using a random mutation rate drawn from a heavy-tailed distribution ("fast mutation", Doerr et al. (2017)) and increasing the mutation strength based on stagnation detection (Rajabi and Witt (2020)). Whereas the latter can obtain the asymptotically best probability of finding a single desired solution in a given distance, the former is more robust and performs much better when many improving solutions in some distance exist. In this work, we propose a mutation strategy that combines ideas of both mechanisms. We show that it can also obtain the best possible probability of finding a single distant solution. However, when several improving solutions exist, it can outperform both the stagnation-detection approach and fast mutation. The new operator is more than an interleaving of the two previous mechanisms and it also outperforms any such interleaving.
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。
