Real-world problems are often comprised of many objectives and require solutions that carefully trade-off between them. Current approaches to many-objective optimization often require challenging assumptions, like knowledge of the importance/difficulty of objectives in a weighted-sum single-objective paradigm, or enormous populations to overcome the curse of dimensionality in multi-objective Pareto optimization. Combining elements from Many-Objective Evolutionary Algorithms and Quality Diversity algorithms like MAP-Elites, we propose Many-objective Optimization via Voting for Elites (MOVE). MOVE maintains a map of elites that perform well on different subsets of the objective functions. On a 14-objective image-neuroevolution problem, we demonstrate that MOVE is viable with a population of as few as 50 elites and outperforms a naive single-objective baseline. We find that the algorithm's performance relies on solutions jumping across bins (for a parent to produce a child that is elite for a different subset of objectives). We suggest that this type of goal-switching is an implicit method to automatic identification of stepping stones or curriculum learning. We comment on the similarities and differences between MOVE and MAP-Elites, hoping to provide insight to aid in the understanding of that approach $\unicode{x2013}$ and suggest future work that may inform this approach's use for many-objective problems in general.

翻译：现实问题通常包含多个目标，需要解决方案仔细权衡这些目标。当前的多目标优化方法往往需要具有挑战性的假设，例如在加权单目标范式中了解目标的重要性/难度，或需要庞大的种群来克服多目标帕累托优化中的维度灾难。结合多目标进化算法与MAP-Elites等质量多样性算法的元素，我们提出基于精英投票的多目标优化（MOVE）。MOVE维护一个精英图谱，这些精英在不同目标函数子集上表现良好。在14目标的图像神经进化问题上，我们证明了MOVE仅需50个精英的种群即可可行，并优于简单的单目标基线。我们发现算法性能依赖于解在分箱间的跳跃（即父代产生对另一目标子集为精英的子代）。我们认为这种目标切换类型是自动识别垫脚石或课程学习的隐式方法。我们评论了MOVE与MAP-Elites的异同，期望为该方法的理解提供见解——并建议未来工作可推动该方法在一般多目标问题中的应用。