Bayesian Optimization of Multiple Objectives with Different Latencies
- URL: http://arxiv.org/abs/2302.01310v1
- Date: Thu, 2 Feb 2023 18:33:34 GMT
- Title: Bayesian Optimization of Multiple Objectives with Different Latencies
- Authors: Jack M. Buckingham, Sebastian Rojas Gonzalez and Juergen Branke
- Abstract summary: In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective.
We propose a scalarization based knowledge gradient acquisition function which accounts for the different evaluation costs of the objectives.
We prove consistency of the algorithm and show empirically that it significantly outperforms a benchmark algorithm which always evaluates both objectives.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Multi-objective Bayesian optimization aims to find the Pareto front of
optimal trade-offs between a set of expensive objectives while collecting as
few samples as possible. In some cases, it is possible to evaluate the
objectives separately, and a different latency or evaluation cost can be
associated with each objective. This presents an opportunity to learn the
Pareto front faster by evaluating the cheaper objectives more frequently. We
propose a scalarization based knowledge gradient acquisition function which
accounts for the different evaluation costs of the objectives. We prove
consistency of the algorithm and show empirically that it significantly
outperforms a benchmark algorithm which always evaluates both objectives.
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