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.

使用 Many-Objective Evolutionary Algorithms 和 Quality Diversity algorithms 的元素，我们提出了基于投票的多目标精英优化 (MOVE)。MOVE 通过维护适用于目标函数不同子集的精英群体地图来平衡众多目标，并通过在具有 14 个目标的图像神经进化问题上的实验证明 MOVE 是可行的。我们发现该算法的性能取决于解决方案在不同目标子集之间跳跃，并指出这种目标切换是一种隐式的自动识别逐步学习的方法。我们对 MOVE 和 MAP-Elites 之间的相似之处和差异进行了讨论，并提出未来的研究方向以供参考，以便更好地理解这种方法在众多目标问题中的应用。