On the use of Hinge Loss as a Surrogate Fitness Function with Grammatical Evolution for Parkinson's Disease Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8506

@InProceedings{duan:2025:GECCOcomp,

• author = "Jiajun Duan and Miguel Nicolau and Michael O'Neill",
• title = "On the use of Hinge Loss as a Surrogate Fitness Function with Grammatical Evolution for Parkinson's Disease Classification",
• booktitle = "Symbolic Regression",
• year = "2025",
• editor = "Gabriel Kronberger and Fabricio {Olivetti de Franca} William {La Cava} and Steven Gustafson",
• pages = "2514--2520",
• 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, surrogate method, Parkinson's disease, symbolic regression",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734348",
• DOI = "doi:10.1145/3712255.3734348",
• size = "7 pages",
• abstract = "Parkinson's Disease (PD) is a progressive neurodegenerative disorder of the nervous system with a high rate of misdiagnosis. In this paper, we propose a Grammatical Evolution (GE) approach for classifying PD patients, that uses hinge loss as a surrogate fitness function. We compare our approach to standard GE using accuracy as its fitness function. Our results demonstrate that the surrogate fitness approach consistently produces models with better accuracy and reduced complexity. These results highlight the potential of using our approach as a powerful tool for developing trustworthy AI applications in the medical domain.",
• notes = "GECCO-2025 SymReg workshop A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jiajun Duan Miguel Nicolau Michael O'Neill

Citations