论文标题
重新审视非精英进化多目标优化器
Non-elitist Evolutionary Multi-objective Optimizers Revisited
论文作者
论文摘要
自2000年左右以来，人们认为，精英进化多目标优化算法（EMOAS）总是比非享受的表情符号。本文使用无界外部存档时，重新审视非私人表情符号的双向持续优化的表现。本文通过两种精英主义者和一项非精明主义的环境选择来研究表情符号的表现。在可可平台提供的双目标BBOB问题套件上评估了EMOAS的性能。与传统的智慧相反，结果表明，使用无限的外部存档时，具有特定交叉方法的非专业表情符号在BI-Obignive BBOB问题上具有许多决策变量。本文还分析了非精明主义选择的特性。
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the performance of non-elitist EMOAs for bi-objective continuous optimization when using an unbounded external archive. This paper examines the performance of EMOAs with two elitist and one non-elitist environmental selections. The performance of EMOAs is evaluated on the bi-objective BBOB problem suite provided by the COCO platform. In contrast to conventional wisdom, results show that non-elitist EMOAs with particular crossover methods perform significantly well on the bi-objective BBOB problems with many decision variables when using the unbounded external archive. This paper also analyzes the properties of the non-elitist selection.
