ExperienceThinking: Constrained Hyperparameter Optimization based on Knowledge and Pruning

TL;DR

ExperienceThinking is a novel hyperparameter optimization method that efficiently finds optimal configurations by leveraging knowledge and pruning, significantly reducing evaluation costs and outperforming classical algorithms.

Contribution

The paper introduces the ExperienceThinking algorithm, combining search space pruning and knowledge utilization for constrained hyperparameter optimization.

Findings

Outperforms three classical optimization algorithms with fewer evaluations

Achieves superior hyperparameter configurations in experiments

Reduces computational cost of hyperparameter tuning

Abstract

Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, and thus reduce computational cost and promote optimization efficiency. Motivated by this, we propose ExperienceThinking algorithm to quickly find the best possible hyperparameter configuration of machine learning algorithms within a few configuration evaluations. ExperienceThinking design two novel methods, which intelligently infer optimal configurations from two aspects: search space pruning and knowledge utilization respectively. Two methods complement each other and solve the constrained hyperparameter optimization problems effectively. To demonstrate the benefit of ExperienceThinking, we compare it with 3 classical hyperparameter optimization algorithms with a small number of configuration evaluations. The experimental results present that our proposed algorithm provides superior results and achieve better performance.