The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Julio Albinati and Gisele L. Pappa and 
                 Fernando E. B. Otero and Luiz Otavio V. B. Oliveira",
  title =        "The Effect of Distinct Geometric Semantic Crossover
                 Operators in Regression Problems",
  booktitle =    "18th European Conference on Genetic Programming",
  year =         "2015",
  editor =       "Penousal Machado and Malcolm I. Heywood and 
                 James McDermott and Mauro Castelli and 
                 Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
  series =       "LNCS",
  volume =       "9025",
  publisher =    "Springer",
  pages =        "3--15",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Crossover,
                 Crossover mask optimisation",
  isbn13 =       "978-3-319-16500-4",
  DOI =          "doi:10.1007/978-3-319-16501-1",
  abstract =     "This paper investigates the impact of geometric
                 semantic crossover operators in a wide range of
                 symbolic regression problems. First, it analyses the
                 impact of using Manhattan and Euclidean distance
                 geometric semantic crossovers in the learning process.
                 Then, it proposes two strategies to numerically
                 optimise the crossover mask based on mathematical
                 properties of these operators, instead of simply
                 generating them randomly. An experimental analysis
                 comparing geometric semantic crossovers using Euclidean
                 and Manhattan distances and the proposed strategies is
                 performed in a test bed of twenty datasets. The results
                 show that the use of different distance functions in
                 the semantic geometric crossover has little impact on
                 the test error, and that our optimized crossover masks
                 yield slightly better results. For SGP practitioners,
                 we suggest the use of the semantic crossover based on
                 the Euclidean distance, as it achieved similar results
                 to those obtained by more complex operators.",
  notes =        "Nominated for EuroGP 2015 Best Paper.

                 Part of \cite{Machado:2015:GP} EuroGP'2015 held in
                 conjunction with EvoCOP2015, EvoMusArt2015 and

Genetic Programming entries for Julio Albinati Gisele L Pappa Fernando Esteban Barril Otero Luiz Otavio Vilas Boas Oliveira