How to Exploit Alignment in the Error Space: Two Different GP Models

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

  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Sara Silva and Stefano Ruberto",
  title =        "How to Exploit Alignment in the Error Space: Two
                 Different GP Models",
  booktitle =    "Genetic Programming Theory and Practice XII",
  year =         "2014",
  editor =       "Rick Riolo and William P. Worzel and Mark Kotanchek",
  series =       "Genetic and Evolutionary Computation",
  pages =        "133--148",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Error space, Geometry",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_8",
  abstract =     "From a recent study, we know that if we are able to
                 find two optimally aligned individuals, then we can
                 reconstruct a globally optimal solution analytically
                 for any regression problem. With this knowledge in
                 mind, the objective of this chapter is to discuss two
                 Genetic Programming (GP) models aimed at finding pairs
                 of optimally aligned individuals. The first one of
                 these models, already introduced in a previous
                 publication, is SGP-1. The second model, discussed for
                 the first time here, is called Pair Optimisation GP
                 (POGO). The main difference between these two models is
                 that, while SGP-1 represents solutions in a traditional
                 way, as single expressions (as in standard GP), in POGO
                 individuals are pairs of expressions, that evolution
                 should push towards the optimal alignment. The results
                 we report for both these models are extremely
                 encouraging. In particular, ESAGP-1 outperforms
                 standard GP and geometric semantic GP on two complex
                 real-life applications. At the same time, a preliminary
                 set of results obtained on a set of symbolic regression
                 benchmarks indicate that POGP, although rather new and
                 still in need of improvement, is a very promising
                 model, that deserves future developments and
  notes =        "

                 Part of \cite{Riolo:2014:GPTP} published after the
                 workshop in 2015",

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