Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

TL;DR

This paper introduces PPES, a novel non-greedy batch Bayesian optimization algorithm that efficiently selects multiple points to evaluate in parallel, improving optimization of expensive black-box functions.

Contribution

PPES is the first non-greedy batch Bayesian optimization method, enhancing parallel evaluation efficiency for expensive black-box functions.

Findings

PPES outperforms greedy methods in synthetic benchmarks.

PPES demonstrates improved optimization in real-world applications.

PPES effectively handles high-dimensional problems.

Abstract

We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.