A Multiple Expression Alignment Framework for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Vanneschi:2018:EuroGP,

• author = "Leonardo Vanneschi and Kristen Scott and Mauro Castelli",
• title = "A Multiple Expression Alignment Framework for Genetic Programming",
• booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming",
• year = "2018",
• month = "4-6 " # apr,
• editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
• series = "LNCS",
• volume = "10781",
• publisher = "Springer Verlag",
• address = "Parma, Italy",
• pages = "166--183",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-77552-4",
• DOI = "doi:10.1007/978-3-319-77553-1_11",
• abstract = "Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. As a consequence, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. In this paper, we critically discuss those methods, analysing their major limitations and we propose new genetic programming systems aimed at overcoming those limitations. The presented experimental results, conducted on five real-life symbolic regression problems, show that the proposed algorithms outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.",
• notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018",

}

Genetic Programming entries for Leonardo Vanneschi Kristen M Scott Mauro Castelli

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