AI 中文总结
研究目标数量增长及目标交互性质对算法性能的影响，采用诊断基准套件评估多种进化算法，发现问题特征显著影响算法性能，词法选择算法有优势且避免对预定义参考方向的依赖。
AI 中文摘要
在过去二十年中，多目标优化一直是一个备受关注的领域，已经引入了几种进化优化算法来解决这些问题；然而，有两个基本问题仍未得到充分探索：（i）当目标数量增长超过典型的约十五个目标的多目标范围并变得庞大时会发生什么？（ii）问题特征，例如目标之间的交互性质，如何影响算法性能？为了回答这些问题，我们采用了一个诊断基准套件，该套件允许控制问题特征并且可以扩展到极高的目标数量。使用这个框架，我们在一系列维度和诊断问题景观上评估了几种最先进的进化算法，包括NSGA-II、NSGA-III、MOEA/D和词法选择。我们的实验表明，问题特征会显著影响算法性能。特别是，目标之间的交互性质似乎很重要。这些结果突出了在为特定问题选择算法之前理解这些属性的重要性。我们还表明，词法选择，一种最初为遗传编程设计的算法，与最先进的多目标优化算法相比具有优势，同时避免了对预定义参考方向的依赖。
英文摘要
Many-objective optimization has been a field of interest over the past two decades and several evolutionary optimization algorithms have been introduced to tackle these problems; yet two fundamental questions remain underexplored: (i) What happens when the number of objectives grows beyond the typical many-objective regime of about fifteen and becomes massive? (ii) How do problem characteristics, such as the nature of interactions between objectives, influence algorithmic performance? To answer these questions we employ a diagnostic benchmark suite that allows control over problem characteristics and can be scaled to extremely high objective counts. Using this framework we evaluate several state-of-the-art evolutionary algorithms including NSGA-II, NSGA-III, MOEA/D and lexicase selection across a range of dimensionalities and diagnostic problem landscapes. Our experiments reveal that problem characteristics significantly affect algorithm performance. In particular, the nature of interactions between objectives appears important. These results highlight the importance of understanding these properties before selecting an algorithm for a specific problem. We also show that lexicase selection, an algorithm originally designed for genetic programming, compares favorably with state-of-the-art many-objective optimization algorithms while avoiding the dependence on predefined reference directions.
