Use of two-layer genetic programming for multidimensional symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8355

@InProceedings{Merta:2024:Informatics,

• author = "Jan Merta and Tomas Brandejsky",
• title = "Use of two-layer genetic programming for multidimensional symbolic regression",
• booktitle = "2024 IEEE 17th International Scientific Conference on Informatics (Informatics)",
• year = "2024",
• pages = "487--492",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Benchmark testing, Informatics, Python, two-layer genetic programming, symbolic regression, multi-dimensional data, benchmarks",
• DOI = "doi:10.1109/Informatics62280.2024.10900766",
• abstract = "This paper focuses on exploring the potential benefits and advantages or disadvantages of a two-layer approach in genetic programming. The first section describes two-layer genetic programming itself and how it differs from its basic version. The Python programming language framework DEAP was used for the implementation. The focus of the paper is also to compare the results obtained by using this two-layer genetic programming with different configurations of the parameters with ordinary basic genetic programming on different multidimensional datasets and benchmarks.",
• notes = "Also known as \cite{10900766}",

}

Genetic Programming entries for Jan Merta Tomas Brandejsky

Citations