Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization

TL;DR

This paper introduces an adaptive Bayesian optimization method that dynamically expands the search space during optimization, effectively balancing exploration and exploitation, and demonstrating superior performance on synthetic and hyperparameter tuning tasks.

Contribution

The proposed approach allows Bayesian optimization to adaptively expand its search space, overcoming fixed bounds limitations and improving optimization performance.

Findings

Outperforms existing methods on synthetic functions

Effective in hyperparameter tuning for neural networks

Balances exploration and exploitation adaptively

Abstract

Bayesian optimization is normally performed within fixed variable bounds. In cases like hyperparameter tuning for machine learning algorithms, setting the variable bounds is not trivial. It is hard to guarantee that any fixed bounds will include the true global optimum. We propose a Bayesian optimization approach that only needs to specify an initial search space that does not necessarily include the global optimum, and expands the search space when necessary. However, over-exploration may occur during the search space expansion. Our method can adaptively balance exploration and exploitation in an expanding space. Results on a range of synthetic test functions and an MLP hyperparameter optimization task show that the proposed method out-performs or at least as good as the current state-of-the-art methods.