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

2024-11-01

arXiv.org

AI Summary

Existing research on Meta-Black-Box Optimization (MetaBBO) lacks systematic taxonomies and reproducible implementation guidance. Method: We propose the first unified MetaBBO paradigm, establishing a four-category task taxonomy—algorithm selection, configuration, operation, and generation—and integrate multi-paradigm methodologies including reinforcement learning, supervised learning, neural evolution, and large language model context learning to distill core design principles for enhancing generalization and learning efficiency. Contribution/Results: We conduct empirical evaluations of mainstream MetaBBO methods across performance, computational efficiency, and cross-task generalization. Furthermore, we release a structured practical guide and an actively maintained open-source repository (Awesome-MetaBBO), bridging the critical gap between theoretical unification and engineering deployment.

Technology Category

Search and Optimization: Metareasoning and MetaheuristicsMachine Learning: OptimizationMultiagent Systems: Mechanism Design

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingEconomics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

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.

Problem

• Summarizing key aspects of MetaBBO for automated algorithm design
• Providing practical guidance for implementing MetaBBO approaches
• Evaluating performance and generalization of MetaBBO methods

Innovation

• Meta-learning for automated algorithm design
• Unified taxonomy for algorithm design tasks
• Comprehensive evaluation of MetaBBO methods