基于自回归学习的自动化元启发式算法设计
Automated Metaheuristic Algorithm Design with Autoregressive Learning
AI总结：
本文提出基于自回归生成网络的元启发式算法自动设计方法，将算法设计建模为序列生成任务，能够生成多样结构并复用先验设计知识，在25个测试问题中的24个上超越人类设计的基线算法。
摘要：
元启发式算法的自动化设计提供了一条有吸引力的途径，可以减少人力投入并获得超越人类直觉的增强性能。当前的自动化方法在固定结构内设计算法，并且从零开始操作。这在充分发现元启发式算法家族的潜力以及从先前设计经验中汲取养分方面存在明显差距。为了弥合这一差距，本文提出了一种基于自回归学习的设计器，用于元启发式算法的自动化设计。我们的设计器将元启发式算法设计形式化为一个序列生成任务，并利用自回归生成网络来处理该任务。这带来了两个进展。首先，通过自回归推理，设计器生成具有不同长度和结构的算法，从而能够充分发现元启发式算法家族的潜力。其次，设计器神经元中学习和积累的先前设计知识可以被检索出来，用于为未来问题设计算法，为面向开放式问题求解的持续算法设计铺平道路。在数值基准和现实世界问题上的大量实验表明，所提出的设计器生成的算法在25个测试问题中的24个上优于所有人类创建的基线。生成的算法展示了各种结构和行为，合理地适应不同的问题求解情境。代码将在论文发表后发布。
英文摘要：
Automated design of metaheuristic algorithms offers an attractive avenue to reduce human effort and gain enhanced performance beyond human intuition. Current automated methods design algorithms within a fixed structure and operate from scratch. This poses a clear gap towards fully discovering potentials over the metaheuristic family and fertilizing from prior design experience. To bridge the gap, this paper proposes an autoregressive learning-based designer for automated design of metaheuristic algorithms. Our designer formulates metaheuristic algorithm design as a sequence generation task, and harnesses an autoregressive generative network to handle the task. This offers two advances. First, through autoregressive inference, the designer generates algorithms with diverse lengths and structures, enabling to fully discover potentials over the metaheuristic family. Second, prior design knowledge learned and accumulated in neurons of the designer can be retrieved for designing algorithms for future problems, paving the way to continual design of algorithms for open-ended problem-solving. Extensive experiments on numeral benchmarks and real-world problems reveal that the proposed designer generates algorithms that outperform all human-created baselines on 24 out of 25 test problems. The generated algorithms display various structures and behaviors, reasonably fitting for different problem-solving contexts. Code will be released after paper publication.
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。
