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.

翻译：元启发式算法的自动设计为减少人工投入并在人类直觉之外获得更优性能提供了有吸引力的途径。当前自动化方法在固定结构内从头开始设计算法，这明显缺乏对元启发式算法家族潜力的全面发掘以及从先验设计经验中汲取养分的能力。为弥补这一差距，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设计。该设计器将元启发式算法设计形式化为序列生成任务，并利用自回归生成网络处理该任务。这带来了两大进步：首先，通过自回归推理，设计器可生成具有不同长度和结构的算法，从而充分发掘元启发式算法家族的潜力；其次，设计器神经元中学习积累的先验设计知识可被检索用于解决未来问题的算法设计，为持续设计应对开放问题的算法铺平道路。在数值基准测试和实际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个上优于所有人工设计的基准方法。生成的算法展现出多样化的结构和行为，能够合理适应不同问题的求解场景。论文发表后将公开相关代码。