This study investigates the feasibility and effectiveness of using Large Language Models (LLMs) to drive the evolution of agent-based rules in computational modeling systems.
@inproceedings{jwalapuram2025lear,title={LEAR: LLM-Driven Evolution of Agent-Based Rules},author={Gurkan, Can and Jwalapuram, Narasimha Karthik and Wang, Kevin and Danda, Rudy and Rasmussen, Leif and Chen, John and Wilensky, Uri},year={2025},month=jul,day={14},booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},series={GECCO '25 Companion},publisher={Association for Computing Machinery},address={New York, NY, USA},doi={10.1145/3712255.3734368},url={https://doi.org/10.1145/3712255.3734368},}