A portfolio approach to massively parallel Bayesian optimization

• URL: http://arxiv.org/abs/2110.09334v2
• Date: Mon, 3 Apr 2023 09:44:41 GMT
• Title: A portfolio approach to massively parallel Bayesian optimization
• Authors: Mickael Binois (ACUMES, JAD), Nicholson Collier (ANL), Jonathan Ozik (ANL)
• Abstract summary: One way to reduce the time of conducting optimization studies is to evaluate designs rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.

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