Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Luiz Otavio V. B. Oliveira and 
                 Luis Fernando Miranda and Gisele L. Pappa and Fernando E. B. Otero and 
                 Ricardo H. C. Takahashi",
  title =        "Reducing Dimensionality to Improve Search in Semantic
                 Genetic Programming",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "375--385",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming,
                 Dimensionality reduction, Instance selection",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_35",
  size =         "11 pages",
  abstract =     "Genetic programming approaches are moving from
                 analysing the syntax of individual solutions to look
                 into their semantics. One of the common definitions of
                 the semantic space in the context of symbolic
                 regression is a n-dimensional space, where n
                 corresponds to the number of training examples. In
                 problems where this number is high, the search process
                 can became harder as the number of dimensions increase.
                 Geometric semantic genetic programming (GSGP) explores
                 the semantic space by performing geometric semantic
                 operations—the fitness landscape seen by GSGP is
                 guaranteed to be conic by construction. Intuitively, a
                 lower number of dimensions can make search more
                 feasible in this scenario, decreasing the chances of
                 data overfitting and reducing the number of evaluations
                 required to find a suitable solution. This paper
                 proposes two approaches for dimensionality reduction in
                 GSGP: (i) to apply current instance selection methods
                 as a pre-process step before training points are given
                 to GSGP; (ii) to incorporate instance selection to the
                 evolution of GSGP. Experiments in 15 datasets show that
                 GSGP performance is improved by using instance
                 reduction during the evolution.",
  notes =        "p382 'TCNN and TENN ... no better than ... a random


Genetic Programming entries for Luiz Otavio Vilas Boas Oliveira Luis Fernando Miranda Gisele L Pappa Fernando Esteban Barril Otero Ricardo Hiroshi Caldeira Takahashi