OpenBox: A Python Toolkit for Generalized Black-box Optimization
Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang,, Ce Zhang, Bin Cui
TL;DR
OpenBox is a user-friendly, modular Python toolkit for black-box optimization that improves applicability, performance, and efficiency in various applications like machine learning and experimental design.
Contribution
It introduces a flexible, open-source toolkit with enhanced usability, visualization, and modular design for generalized black-box optimization tasks.
Findings
Demonstrates improved effectiveness over existing systems
Shows increased efficiency in optimization tasks
Provides a versatile, user-friendly interface
Abstract
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning. However, users still face challenges when applying BBO methods to their problems at hand with existing software packages in terms of applicability, performance, and efficiency. This paper presents OpenBox, an open-source BBO toolkit with improved usability. It implements user-friendly interfaces and visualization for users to define and manage their tasks. The modular design behind OpenBox facilitates its flexible deployment in existing systems. Experimental results demonstrate the effectiveness and efficiency of OpenBox over existing systems. The source code of OpenBox is available at https://github.com/PKU-DAIR/open-box.
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Advanced Bandit Algorithms Research · Machine Learning and Data Classification