Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization

In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on evolutionary computation p. 1
Main Authors: Ma, Zeyuan, Guo, Hongshu, Gong, Yue-Jiao, Zhang, Jun, Tan, Kay Chen
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Black-Box-Optimization
Evolutionary Computation
Glass box
Heuristic algorithms
Learning to Optimize
Meta-Black-Box-Optimization
Metalearning
Optimization
Performance gain
Recurrent neural networks
Reviews
Supervised learning
Surveys
Training
ISSN:1089-778X, 1941-0026
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this survey, we introduce Meta-Black-Box-Optimization (MetaBBO) as an emerging avenue within the Evolutionary Computation (EC) community, which incorporates Meta-learning approaches to assist automated algorithm design. Despite the success of MetaBBO, the current literature provides insufficient summaries of its key aspects and lacks practical guidance for implementation. To bridge this gap, we offer a comprehensive review of recent advances in MetaBBO, providing an in-depth examination of its key developments. We begin with a unified definition of the MetaBBO paradigm, followed by a systematic taxonomy of various algorithm design tasks, including algorithm selection, algorithm configuration, solution manipulation, and algorithm generation. Further, we conceptually summarize different learning methodologies behind current MetaBBO works, including reinforcement learning, supervised learning, neuroevolution, and in-context learning with Large Language Models. A comprehensive evaluation of the latest representative MetaBBO methods is then carried out, alongside an experimental analysis of their optimization performance, computational efficiency, and generalization ability. Based on the evaluation results, we meticulously identify a set of core designs that enhance the generalization and learning effectiveness of MetaBBO. Finally, we outline the vision for the field by providing insight into the latest trends and potential future directions. Relevant literature will be continuously collected and updated at https://github.com/MetaEvo/Awesome-MetaBBO.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2025.3568053