Grammar-based Vectorial Genetic Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Fleck:2021:GPTP,

• author = "Philipp Fleck and Stephan Winkler and Michael Kommenda and Michael Affenzeller",
• title = "Grammar-based Vectorial Genetic Programming for Symbolic Regression",
• booktitle = "Genetic Programming Theory and Practice XVIII",
• year = "2021",
• editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• pages = "21--43",
• address = "East Lansing, USA",
• month = "19-21 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-16-8112-7",
• DOI = "doi:10.1007/978-981-16-8113-4_2",
• abstract = "Vectorial Genetic Programming (GP) is a young branch of GP, where the training data for symbolic models not only include regular, scalar variables, but also allow vector variables. Also, the models abilities are extended to allow operations on vectors, where most vector operations are simply performed component-wise. Additionally, new aggregation functions are introduced that reduce vectors into scalars, allowing the model to extract information from vectors by itself, thus eliminating the need of prior feature engineering that is otherwise necessary for traditional GP to use vector data. And due to the white-box nature of symbolic models, the operations on vectors can be as easily interpreted as regular operations on scalars. In this paper, we extend the ideas of vectorial GP of previous authors, and propose a grammar-based approach for vectorial GP that can deal with various challenges noted. To evaluate grammar-based vectorial GP, we have designed new benchmark functions that contain both scalar and vector variables, and show that traditional GP falls short very quickly for certain scenarios. Grammar-based vectorial GP, however, is able to solve all presented benchmarks.",
• notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022",

}

Genetic Programming entries for Philipp Fleck Stephan M Winkler Michael Kommenda Michael Affenzeller

Citations