A Multiple Expression Alignment Framework for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Leonardo Vanneschi and Kristen Scott and 
                 Mauro Castelli",
  title =        "A Multiple Expression Alignment Framework for Genetic
  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
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Leonardo Vanneschi Kristen Scott Mauro Castelli