Covariance Matrix Adaptation MAP-Annealing
Covariance Matrix Adaptation MAP-Annealing
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DOI：
10.1145/3583131.3590389
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Matthew C. Fontaine;S. Nikolaidis
中科院分区：
文献类型：
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作者：
Matthew C. Fontaine;S. Nikolaidis
单目标优化算法针对目标搜索单个最高质量的解决方案。质量多样性 (QD) 优化算法，例如协方差矩阵适应 MAP-Elites (CMA-ME)，搜索一系列解决方案，这些解决方案在目标方面既是高质量的，又是在指定测量函数方面具有多样性的。然而，CMA-ME 受到 QD 社区强调的三个主要限制：过早地放弃目标以支持探索、努力探索平面目标以及低分辨率档案的性能不佳。我们提出了一种新的质量多样性算法，即协方差矩阵适应 MAP 退火 (CMA-MAE)，它解决了所有三个限制。我们针对每个限制为新算法提供了理论依据。我们的理论为我们的实验提供了依据，这些实验支持了该理论并表明 CMA-MAE 实现了最先进的性能和鲁棒性。
Single-objective optimization algorithms search for the single highest-quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high-quality with respect to an objective and diverse with respect to specified measure functions. However, CMA-ME suffers from three major limitations highlighted by the QD community: prematurely abandoning the objective in favor of exploration, struggling to explore flat objectives, and having poor performance for low-resolution archives. We propose a new quality diversity algorithm, Covariance Matrix Adaptation MAP-Annealing (CMA-MAE), that addresses all three limitations. We provide theoretical justifications for the new algorithm with respect to each limitation. Our theory informs our experiments, which support the theory and show that CMA-MAE achieves state-of-the-art performance and robustness.