🤖 AI Summary
Manual design of metaheuristic algorithms is time-consuming, inefficient, and structurally inflexible; existing automated approaches are constrained by fixed algorithmic templates and linear representations. Method: This paper proposes the first general-purpose automated design framework applicable to the entire family of metaheuristics. It introduces (1) a unified algorithmic prototype covering all metaheuristic variants; (2) a directed acyclic graph (DAG)-based representation enabling structural evolution; and (3) a compact yet expressive graph embedding and differentiable architecture encoding scheme. Integrating graph representation learning with evolutionary search, the framework enables end-to-end generation of diverse, nonlinear algorithmic structures. Results: Extensive evaluation on numerical optimization benchmarks and real-world tasks demonstrates that the automatically generated algorithms achieve superior efficiency, generalizability, and novelty compared to both human-designed and state-of-the-art automated methods.
📝 Abstract
Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.
