The workshop will start with an introduction tutorial, followed by an invited talk and by the oral presentations of accepted workshop articles. More details on the program will follow.

Introductory tutorial

In this brief tutorial, participants will receive an overview of Graph-based Genetic Programming (GGP). The session will cover fundamental concepts, methodologies, and potential applications across different domains. Additionally, we will explore popular code frameworks used in GGP implementations, highlighting their key features and functionalities.

Invited talks

“Evolutionary Optimization of Model Merging Recipes”

With the increased diffusion of large and diverse models, Evolutionary Model Merge is proposed as a general method that uses evolutionary techniques to efficiently discover the best ways to combine different models with diverse capabilities, using ideas similar to graph-based neural architecture search.

“Evolutionary Robustness”

William B. Langdon

University College London, UK

Oral presentations

Byron: A Fuzzer for Turing-complete Test Programs

Marco Sacchet, Dimitri Masetta, Giovanni Squillero, Alberto Tonda

Directed Acyclic Program Graph Applied to Supervised Classification

Thibaut Bellanger, Matthieu Le Berre, Manuel Clergue, Jin-Kao Hao

On Search Trajectory Networks for Graph Genetic Programming

Camilo De La Torre, Sylvain Cussat-Blanc, Dennis Wilson, Yuri Lavinas

Minimizing the EXA-GP Graph-Based Genetic Programming Algorithm for Interpretable Time Series Forecasting

Jared Murphy, Travis Desell