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

TL;DR

This survey provides a comprehensive overview of Meta-Black-Box-Optimization, highlighting recent advances, key methodologies, evaluation results, and future directions in automating algorithm design using meta-learning techniques.

Contribution

It offers the first systematic taxonomy, conceptual summaries of methodologies, and an experimental evaluation of MetaBBO, along with practical guidance for implementation.

Findings

MetaBBO effectively automates algorithm design tasks.

Recent methods show promising generalization and efficiency.

Core design principles improve MetaBBO performance.

Abstract

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.