分析约束搜索空间中竞争性进化策略的设计原则
Analyzing design principles for competitive evolution strategies in constrained search spaces
AI总结：
本文针对约束优化竞赛中表现优异的$ε$MAg-ES进行实证分析，以揭示各算法组件的性能贡献，并在排名中引入显著性检验。
摘要：
在2018年IEEE进化计算大会的背景下，用于约束优化的矩阵自适应进化策略在约束单目标实参数优化竞赛中表现得尤为成功。在所有考虑的实例中，所谓的$ε$MAg-ES获得了第二名。然而，它可以被认为是高维问题中最成功的参与者。遗憾的是，竞赛结果并未提供有关成功算法的运作方式，或其对特定形态问题适用性的任何信息。为此，本论文关注对$ε$MAg-ES工作原理的广泛实证分析，预期可揭示特定算法组件对性能的贡献。为避免因算法实现中不显著的差异而形成排名，本文还在排名过程中引入了显著性检验。
英文摘要：
In the context of the 2018 IEEE Congress of Evolutionary Computation, the Matrix Adaptation Evolution Strategy for constrained optimization turned out to be notably successful in the competition on constrained single objective real-parameter optimization. Across all considered instances the so-called $ε$MAg-ES achieved the second rank. However, it can be considered to be the most successful participant in high dimensions. Unfortunately, the competition result does not provide any information about the modus operandi of a successful algorithm or its suitability for problems of a particular shape. To this end, the present paper is concerned with an extensive empirical analysis of the $ε$MAg-ES working principles that is expected to provide insights about the performance contribution of specific algorithmic components. To avoid rankings with respect to insignificant differences within the algorithm realizations, the paper additionally introduces significance testing into the ranking process.
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。
