Generalization Analysis of Symbolic Regression Algorithms via Stability Theory

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@InProceedings{Fong:2026:GECCO,

• author = "Kei Sen Fong and Mehul Motani",
• title = "Generalization Analysis of Symbolic Regression Algorithms via Stability Theory",
• booktitle = "Proceedings of the 2026 Genetic and Evolutionary Computation Conference",
• year = "2026",
• editor = "Nelishia Pillay and Gabriel Kronberger and Leonardo Trujillo and Ting Hu and Sebastian {Rojas Gonzalez} and Saul {Calderon Ramirez} and Stephan Winkler and Yazmin Maldonado and James McDermott and Jose Manuel {Munoz Contreras} and Giorgia Nadizar and Marcella {Scoczynski Ribeiro Martins} and Hirad Assimi and Mario Andres Munoz and Oliver Cuate and Daniel Hernandez and Yanan Sun and Edgar Galvan and Aniko Ekart and Nadarajen Veerapen",
• address = "San Jose, Costa Rica",
• series = "GECCO '26",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming",
• notes = "GECCO-2026 A Recombination of the 35th International Conference on Genetic Algorithms (ICGA) and the 31st Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Kei Sen Fong Mehul Motani

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