Discovering Many Diverse Solutions with Bayesian Optimization

• URL: http://arxiv.org/abs/2210.10953v4
• Date: Tue, 2 May 2023 21:49:15 GMT
• Title: Discovering Many Diverse Solutions with Bayesian Optimization
• Authors: Natalie Maus and Kaiwen Wu and David Eriksson and Jacob Gardner
• Abstract summary: We propose Rank-Ordered Bayesian Optimization with Trust-regions (ROBOT) ROBOT aims to find a portfolio of high-performing solutions that are diverse according to a user-specified diversity metric. We show that it can discover large sets of high-performing diverse solutions while requiring few additional function evaluations.
• Score: 7.136022698519586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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