---
title: "LiBOG: Lifelong Learning for Black-Box Optimizer Generation"
authors:
  - "Jiyuan Pei"
  - "Yi Mei"
  - "Jialin Liu"
  - "Mengjie Zhang"
doi: "10.24963/ijcai.2025/991"
url: "https://ijcai.org/proceedings/2025/991"
pdf: "https://ijcai.org/proceedings/2025/0991.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "8912-8920"
track: "Main Track"
keywords:
  - "Search: S: Meta-reasoning and meta-heuristics"
  - "Search: S: Algorithm portfolios and configuration"
  - "Search: S: Search and machine learning"
---

# LiBOG: Lifelong Learning for Black-Box Optimizer Generation

Jiyuan Pei, Yi Mei, Jialin Liu, Mengjie Zhang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 8912-8920. DOI: [10.24963/ijcai.2025/991](https://doi.org/10.24963/ijcai.2025/991)

## Abstract

Meta-Black-Box Optimization (MetaBBO) garners attention due to its success in automating the configuration and generation of black-box optimizers, significantly reducing the human effort required for optimizer design and discovering optimizers with higher performance than classic human-designed optimizers. However, existing MetaBBO methods conduct one-off training under the assumption that a stationary problem distribution with extensive and representative training problem samples is pre-available. This assumption is often impractical in real-world scenarios, where diverse problems following shifting distribution continually arise. Consequently, there is a pressing need for methods that can continuously learn from new problems encountered on-the-fly and progressively enhance their capabilities. In this work, we explore a novel paradigm of lifelong learning in MetaBBO and introduce LiBOG, a novel approach designed to learn from sequentially encountered problems and generate high-performance optimizers for Black-Box Optimization (BBO). LiBOG consolidates knowledge both across tasks and within tasks to mitigate catastrophic forgetting. Extensive experiments demonstrate LiBOG's effectiveness in learning to generate high-performance optimizers in a lifelong learning manner, addressing catastrophic forgetting while maintaining plasticity to learn new tasks.