Authors
He Yu, Jing Liu
Published in
Evolutionary computation. Pages 1-27. Oct 31, 2025. Epub Oct 31, 2025.
Abstract
Designing algorithms for optimization problems, no matter heuristic or meta-heuristic, often relies on manual design and domain expertise, limiting their scalability and adaptability. The integration of Large Language Models (LLMs) and Evolutionary Algorithms (EAs) presents a promising new way to overcome these limitations to make optimization be more automated, where LLMs function as dynamic agents capable of generating, refining, and interpreting optimization strategies, while EAs explore complex searching spaces efficiently through evolutionary operators. Since this synergy enables a more efficient and creative searching process, we first review important developments in this direction, and then summarize an LLM-EA paradigm for automated optimization algorithm design. We conduct an in-depth analysis on innovative methods for four key EA modules, namely, individual representation, selection, variation operators, and fitness evaluation, addressing challenges related to optimization algorithm design, particularly from the perspective of LLM prompts, analyzing how the prompt flow evolving with the evolutionary process, adjusting based on evolutionary feedback (e.g., population diversity, convergence rate). Furthermore, we analyze how LLMs, through flexible prompt-driven roles, introduce semantic intelligence into fundamental EA characteristics, including diversity, convergence, adaptability, and scalability. Our systematic review and thorough analysis into the paradigm can help researchers better understand the current research and boost the development of synergizing LLMs with EAs for automated optimization algorithm design.
PMID:
41172088
Bibliographic data and abstract were imported from PubMed on 01 Nov 2025.
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