Agent-based Collaborative Random Search for Hyper-parameter Tuning and Global Function Optimization

TL;DR

This paper introduces an agent-based collaborative random search method for hyper-parameter tuning and global function optimization, demonstrating superior performance over traditional randomized strategies in machine learning and high-dimensional problems.

Contribution

It presents a hierarchical agent-based architecture with adaptive random sampling for efficient hyper-parameter and function optimization, especially in resource-limited scenarios.

Findings

Outperformed existing methods in classification and regression tasks

Achieved better results in high-dimensional optimization problems

Effective with limited computational resources

Abstract

Hyper-parameter optimization is one of the most tedious yet crucial steps in training machine learning models. There are numerous methods for this vital model-building stage, ranging from domain-specific manual tuning guidelines suggested by the oracles to the utilization of general-purpose black-box optimization techniques. This paper proposes an agent-based collaborative technique for finding near-optimal values for any arbitrary set of hyper-parameters (or decision variables) in a machine learning model (or general function optimization problem). The developed method forms a hierarchical agent-based architecture for the distribution of the searching operations at different dimensions and employs a cooperative searching procedure based on an adaptive width-based random sampling technique to locate the optima. The behavior of the presented model, specifically against the changes in its design parameters, is investigated in both machine learning and global function optimization applications, and its performance is compared with that of two randomized tuning strategies that are commonly used in practice. According to the empirical results, the proposed model outperformed the compared methods in the experimented classification, regression, and multi-dimensional function optimization tasks, notably in a higher number of dimensions and in the presence of limited on-device computational resources.