Dynamic Multi-objective Ensemble of Acquisition Functions in Batch
Bayesian Optimization
- URL: http://arxiv.org/abs/2206.11118v1
- Date: Wed, 22 Jun 2022 14:09:18 GMT
- Title: Dynamic Multi-objective Ensemble of Acquisition Functions in Batch
Bayesian Optimization
- Authors: Jixiang Chen and Fu Luo and Zhenkun Wang
- Abstract summary: The acquisition function plays a crucial role in the optimization process.
Three acquisition functions are dynamically selected from a set based on their current and historical performance.
Using an evolutionary multi-objective algorithm to optimize such a MOP, a set of non-dominated solutions can be obtained.
- Score: 1.1602089225841632
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Bayesian optimization (BO) is a typical approach to solve expensive
optimization problems. In each iteration of BO, a Gaussian process(GP) model is
trained using the previously evaluated solutions; then next candidate solutions
for expensive evaluation are recommended by maximizing a cheaply-evaluated
acquisition function on the trained surrogate model. The acquisition function
plays a crucial role in the optimization process. However, each acquisition
function has its own strengths and weaknesses, and no single acquisition
function can consistently outperform the others on all kinds of problems. To
better leverage the advantages of different acquisition functions, we propose a
new method for batch BO. In each iteration, three acquisition functions are
dynamically selected from a set based on their current and historical
performance to form a multi-objective optimization problem (MOP). Using an
evolutionary multi-objective algorithm to optimize such a MOP, a set of
non-dominated solutions can be obtained. To select batch candidate solutions,
we rank these non-dominated solutions into several layers according to their
relative performance on the three acquisition functions. The empirical results
show that the proposed method is competitive with the state-of-the-art methods
on different problems.
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