Discovering Many Diverse Solutions with Bayesian Optimization
Discovering Many Diverse Solutions with Bayesian Optimization
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DOI：
10.48550/arxiv.2210.10953
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
N. Maus;Kaiwen Wu;David Eriksson;J. Gardner
中科院分区：
文献类型：
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作者：
N. Maus;Kaiwen Wu;David Eriksson;J. Gardner
贝叶斯优化（BO）是一种流行的黑盒目标函数的样本有效优化方法。虽然BO已成功地应用于广泛的科学应用，传统的方法，单目标BO只寻求找到一个最佳的解决方案。在解决方案后来可能变得难以处理的情况下，这可能是一个重大的限制。例如，一个设计的分子可能会违反约束，只有在优化过程结束后才能合理地评估。为了解决这个问题，我们提出了秩序贝叶斯优化与信任区域（ROBOT），其目的是找到一个高性能的解决方案，根据用户指定的多样性度量是不同的投资组合。我们评估机器人在几个现实世界的应用程序，并表明它可以发现大量的高性能的不同的解决方案，同时需要很少的额外功能评估相比，找到一个最佳的解决方案。
Bayesian optimization (BO) is a popular approach for sample-efficient optimization of black-box objective functions. While BO has been successfully applied to a wide range of scientific applications, traditional approaches to single-objective BO only seek to find a single best solution. This can be a significant limitation in situations where solutions may later turn out to be intractable. For example, a designed molecule may turn out to violate constraints that can only be reasonably evaluated after the optimization process has concluded. To address this issue, we propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) which aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We evaluate ROBOT on several real-world applications and show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations compared to finding a single best solution.