Versatile Function GPA

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@InProceedings{Brandejsky:2024:Informatics,

• author = "Tomas Brandejsky and Jan Merta",
• title = "Versatile Function {GPA}",
• booktitle = "2024 IEEE 17th International Scientific Conference on Informatics (Informatics)",
• year = "2024",
• pages = "448--452",
• month = nov,
• keywords = "genetic algorithms, genetic programming, Graphics processing units, Evolutionary computation, Big Data applications, Data models, Nonlinear dynamical systems, Informatics, Tuning, Optimisation, hybrid evolutionary algorithm, genetic programming algorithm, versatile function GPA, BigData, symbolic regression",
• DOI = "doi:10.1109/Informatics62280.2024.10900916",
• abstract = "continuous versatile function Genetic Programming Algorithm (GPA) developed with respect to BigData processing. The basic structure of this hierarchical evolutionary algorithm and examples of versatile functions are presented.On the basis of experiments with the hybrid evolutionary algorithm (hybrid EA) providing symbolic regression of precomputed Lorenz attractor system data representing hybrid EA's behaviour, a discussion of examples of an obtained solution is presented. The versatile function concept GPA is applicable, but it requires the hybrid evolutionary algorithm application, as is demonstrated in the paper.",
• notes = "Also known as \cite{10900916}",

}

Genetic Programming entries for Tomas Brandejsky Jan Merta

Citations