Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction
Ana Kostovska, Carola Doerr, Sašo Džeroski, Panče Panov, Tome Eftimov · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2025
Automated algorithm performance prediction in numerical blackbox optimization often relies on problem characterizations, such as exploratory landscape analysis features. These features are typically used as inputs to machine learning models and are represented in a tabular format. However, such approaches often overlook algorithm configurations, a key factor influencing performance. The relationships between algorithm operators, parameters, problem characteristics, and performance outcomes form a complex structure best represented as a graph.