Many-objective Optimization via Voting for Elites
Many-objective Optimization via Voting for Elites
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DOI：
10.1145/3583133.3590693
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Jackson Dean;Nick Cheney
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文献类型：
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作者：
Jackson Dean;Nick Cheney
现实世界的问题通常由许多目标组成，需要在它们之间仔细权衡的解决方案。当前的多目标优化方法通常需要具有挑战性的假设，如在加权和单目标范例中了解目标的重要性/难度，或者需要庞大的总体来克服多目标帕累托优化中的维度诅咒。结合多目标进化算法和MAP-Elites等质量多样性算法，提出了基于精英投票的多目标优化算法(Move)。Move维护在目标函数的不同子集上表现良好的精英的地图。在一个14个目标的图像神经进化问题上，我们证明了MOVE在只有50个精英的情况下是可行的，并且表现优于天真的单目标基线。我们发现，算法的性能依赖于解决方案跨库跳转(父母产生的孩子对于不同的目标子集来说是精英)。我们认为，这种类型的目标转换是自动识别踏脚石或课程学习的一种内隐方法。我们评论了MOVE和MAP-ELITES之间的异同，希望提供见解来帮助理解这一方法-并建议未来的工作，可能有助于将这一方法用于一般的多目标问题。
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 multiobjective 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 - and suggest future work that may inform this approach's use for many-objective problems in general.