ESAGP -- A Semantic GP Framework Based on Alignment in the Error Space

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

  author =       "Stefano Ruberto and Leonardo Vanneschi and 
                 Mauro Castelli and Sara Silva",
  title =        "ESAGP -- A Semantic GP Framework Based on Alignment in
                 the Error Space",
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "150--161",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "doi:10.1007/978-3-662-44303-3_13",
  abstract =     "This paper introduces the concepts of error vector and
                 error space, directly bound to semantics, one of the
                 hottest topics in genetic programming. Based on these
                 concepts, we introduce the notions of optimally aligned
                 individuals and optimally coplanar individuals. We show
                 that, given optimally aligned, or optimally coplanar,
                 individuals, it is possible to construct a globally
                 optimal solution analytically. Thus, we introduce a
                 genetic programming framework for symbolic regression
                 called Error Space Alignment GP (ESAGP) and two of its
                 instances: ESAGP-1, whose objective is to find
                 optimally aligned individuals, and ESAGP-2, whose
                 objective is to find optimally coplanar individuals. We
                 also discuss how to generalise the approach to any
                 number of dimensions. Using two complex real-life
                 applications, we provide experimental evidence that
                 ESAGP-2 outperforms ESAGP-1, which in turn outperforms
                 both standard GP and geometric semantic GP. This
                 suggests that adding dimensions is beneficial and
                 encourages us to pursue the study in many different
                 directions, that we summarise in the final part of the
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",

Genetic Programming entries for Stefano Ruberto Leonardo Vanneschi Mauro Castelli Sara Silva