LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8519

@InProceedings{yu:2025:GECCOcomp2,

• author = "YiMing Yu and Jason Zutty",
• title = "{LLM-Guided} Evolution: An Autonomous Model Optimization for Object Detection",
• booktitle = "Large Language Models for and with Evolutionary Computation Workshop",
• year = "2025",
• editor = "Erik Hemberg and Roman Senkerik and Joel Lehman and Una-May O'Reilly and Michal Pluhacek and Niki {van Stein} and Pier Luca and Tome Eftimov",
• pages = "2363--2370",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, computer aided/automated design, automated machine learning, large language models, neuroevolution, ANN",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734340",
• DOI = "doi:10.1145/3712255.3734340",
• size = "8 pages",
• abstract = "In machine learning, Neural Architecture Search (NAS) requires domain knowledge of model design and a large amount of trial-and-error to achieve promising performance. Meanwhile, evolutionary algorithms have traditionally relied on fixed rules and pre-defined building blocks. The Large Language Model (LLM)-Guided Evolution (GE) framework transformed this approach by incorporating LLMs to directly modify model source code for image classification algorithms on CIFAR data and intelligently guide mutations and crossovers. A key element of LLM-GE is the {"}Evolution of Thought{"} (EoT) technique, which establishes feedback loops, allowing LLMs to refine their decisions iteratively based on how previous operations performed. In this study, we perform NAS for object detection by improving LLM-GE to modify the architecture of You Only Look Once (YOLO) models to enhance performance on the KITTI dataset. Our approach intelligently adjusts the design and settings of YOLO to find the optimal algorithms against objective such as detection accuracy and speed. We show that LLM-GE produced variants with significant performance improvements, such as an increase in Mean Average Precision from 92.5\% to 94.5\%. This result highlights the flexibility and effectiveness of LLM-GE on real-world challenges, offering a novel paradigm for automated machine learning that combines LLM-driven reasoning with evolutionary strategies.",
• notes = "GECCO-2025 LLMfwEC workshop A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for YiMing Yu Jason Zutty

Citations