LiBOG: 面向黑盒优化器生成的终身学习
LiBOG: Lifelong Learning for Black-Box Optimizer Generation
AI总结：
针对现有元黑盒优化依赖平稳问题分布、无法应对现实中持续出现的分布漂移问题的缺陷，提出终身学习方法LiBOG，通过跨任务与任务内知识巩固缓解灾难性遗忘，可从序列问题中持续学习生成高性能黑盒优化器。
摘要：
元黑盒优化（MetaBBO）因在自动化配置与生成黑盒优化器方面取得的成效受到关注，它大幅减少了优化器设计所需的人力投入，还能发现性能优于经典人工设计优化器的算法。然而，现有MetaBBO方法均为一次性训练，其前提假设是预先存在包含大量代表性训练问题样本的平稳问题分布。这一假设在现实场景中通常不切实际——现实中会不断出现遵循漂移分布的多样化问题。因此，迫切需要能够从实时遇到的新问题中持续学习、逐步提升自身能力的方法。本研究探索了MetaBBO领域的终身学习新范式，并提出LiBOG——一种旨在从依次遇到的问题中学习、为黑盒优化（BBO）生成高性能优化器的新方法。LiBOG通过跨任务和任务内的知识巩固来缓解灾难性遗忘问题。大量实验证明，LiBOG在以终身学习方式学习生成高性能优化器方面具备有效性，既能解决灾难性遗忘问题，又能保持学习新任务的可塑性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
