Predicting Heating/Cooling Loads with the Zoetrope Genetic Programming (ZGP) Versus Other Machine Learning Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.8528

@InProceedings{zitar:2024:IntelliSys,

• author = "Raed Abu Zitar and Abdallah Aljasmi and Amal El Fallah Seghrouchni and Frederic Barbaresco",
• title = "Predicting Heating/Cooling Loads with the Zoetrope Genetic Programming {(ZGP)} Versus Other Machine Learning Methods",
• booktitle = "Intelligent Systems and Applications",
• year = "2024",
• editor = "Kohei Arai",
• pages = "386--398",
• address = "Amsterdam, The Netherlands",
• month = "5-6 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://link.springer.com/chapter/10.1007/978-3-031-66336-9_27",
• DOI = "doi:10.1007/978-3-031-66336-9_27",
• abstract = "a comparison between a relatively new regression model called Zoetrope Genetic Programming (ZGP) and traditional machine learning techniques such as Linear Regression, Random Forest, Support Vector Classifier, and Multi Linear Perceptron. The application is a challenging heat load prediction problem with a real data set selected. The ZGP showed comparative results and was in second place for most of the metrics used. The Random forest still showed the best results. Analysis and justifications are shown in the rest of the paper.",
• notes = "Also known as \cite{abuzitar:hal-04951487}",

}

Genetic Programming entries for Raed Abu Zitar Abdallah Aljasmi Amal El Fallah Seghrouchni Frederic Barbaresco

Citations