Semantic Genetic Programming Operators Based on Projections in the Phenotype Space

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Mario Graff and Eric Sadit Tellez and 
                 Elio Villasenor and Sabino Miranda-Jimenez",
  title =        "Semantic Genetic Programming Operators Based on
                 Projections in the Phenotype Space",
  journal =      "Research in Computing Science",
  year =         "2015",
  volume =       "94",
  pages =        "73--85",
  keywords =     "genetic algorithms, genetic programming, semantic
                 crossover, symbolic regression, geometric semantic
                 genetic programming.",
  bibdate =      "2015-06-15",
  bibsource =    "DBLP,
  ISSN =         "1870-4069",
  URL =          "",
  URL =          "",
  size =         "13 pages",
  abstract =     "In the Genetic Programming (GP) community there has
                 been a great interest in developing semantic genetic
                 operators. These type of operators use information of
                 the phenotype to create offspring. The most recent
                 approaches of semantic GP include the GP framework
                 based on the alignment of error space, the geometric
                 semantic genetic operators, and backpropagation genetic
                 operators. Our contribution proposes two semantic
                 operators based on projections in the phenotype space.
                 The proposed operators have the characteristic, by
                 construction, that the offspring's fitness is as at
                 least as good as the fitness of the best parent; using
                 as fitness the euclidean distance. The semantic
                 operators proposed increment the learning capabilities
                 of GP. These operators are compared against a
                 traditional GP and Geometric Semantic GP in the Human
                 oral bioavailability regression problem and 13
                 classification problems. The results show that a GP
                 system with our novel semantic operators has the best
                 performance in the training phase in all the problems

Genetic Programming entries for Mario Graff Guerrero Eric Sadit Tellez Elio Villasenor Sabino Miranda-Jimenez