- author = "Anil Kumar Saini and Jose {Guadalupe Hernandez} and Jason H. Moore",
- title = "{Agentic-GP}: A Theoretical Framework for the Development of Genetic Programming Systems Via Agentic {AI}",
- booktitle = "Genetic Programming Theory and Practice XXII",
- year = "2025",
- editor = "Bogdan Burlacu and Fabricio {Olivetti de Franca} and Alexander Lalejini and Stephen Kelly and Wolfgang Banzhaf",
- series = "Genetic and Evolutionary Computation",
- pages = "373--388",
- address = "Michigan State University, USA",
- month = jun # " 5-7",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-981-95-6397-5",
-
DOI = "
10.1007/978-981-95-6398-2_18",
- abstract = "...for automating this design process. We introduce Agentic GP, a framework in which multiple AI agents , specializing in different GP components, collaborate to build GP systems from scratch based on user-defined requirements. We first describe a general version of the framework applicable to any class of AI agents , and then focus on how that framework can be instantiated using Large Language Model (LLM) agents. We also implemented a simple prototype of the framework using an LLM agent designed to generate selection methods for a given GP system. We evaluated this prototype using a tree-based GP system on a suite of symbolic regression tasks. While it frequently generated selection methods that resulted in runtime errors, the valid methods it produced consistently performed better than random selection but worse than established benchmark methods such as lexicase and tournament selection. This indicates the need for further enhancements in this simple design of the agentic framework.",
-
notes = "lightning talk.
published in 2026 after the workshop",
