Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
Abstract
Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. This survey reviews the bidirectional interaction between these two paradigms and examines how their complementary strengths can be leveraged in hybrid intelligent systems. First, we analyze how EC can enhance LLM-based systems through prompt optimization, hyperparameter tuning, and architecture search. Second, we review how LLMs can im- prove EC by supporting metaheuristic design, surrogate reasoning, adaptive operator control, and heuristic generation. We further discuss emerging co-adaptive frameworks in which LLMs and EC interact through iterative feedback loops. Beyond summarizing recent developments, the survey provides a structured perspec- tive on interaction mechanisms, application patterns, and methodological challenges, including computational cost, reproducibility, interpretability, benchmarking, and generalization. The paper concludes by outlining open research questions and future directions for developing more robust, transparent, and scalable LLM-EC systems.
Authors 5
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National University of Singapore
Affiliation as printed
Department of Electrical and Computer Engineering , National University of Singapore , 119077
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Affiliation as printed
Department of Computer Science , Universidad de Jaén , Spain
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Affiliation as printed
School of Computer and Mathematical Sciences , University of Adelaide - 5005 , Australia
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Leiden University · University of Applied Sciences Leiden
Affiliation as printed
Leiden Institute of Advanced Computer Science , University Leiden , Leiden , Netherlands
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Leiden University · University of Applied Sciences Leiden
Affiliation as printed
Leiden Institute of Advanced Computer Science , University Leiden , Leiden , Netherlands
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