Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Mode
arxiv(2024)
摘要
Heuristics are indispensable for tackling complex search and optimization
problems. However, manual heuristic design is tedious and demands significant
human intuition and experience. This paper introduces Evolution of Heuristic
(EoH), a novel paradigm that leverages the synergy between Large Language
Models (LLMs) and Evolutionary Computation (EC) for Automatic Heuristic Design
(AHD). EoH represents heuristic ideas through linguistic descriptions, termed
thoughts, generated by LLMs, which are then translated into executable code
representations. The coevolution of thoughts and codes within an evolutionary
framework offers superior AHD performance while mitigating computational
expenses. Comprehensive evaluations on three types of combinatorial
optimization benchmarks demonstrate EoH's outperformance against existing AHD
methods. Notably, EoH surpasses FunSearch, a concurrent work focus on code
evolution, identifying superior heuristics with significantly fewer
computational budgets (i.e., queries to LLMs) on online bin packing problem. To
foster reproducibility and accessibility, the source code is
https://github.com/FeiLiu36/EoH.
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