Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression

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@Article{Sotto:2016:Neurocomputing,
  author =       "Leo Francoso dal Piccol Sotto and 
                 Vinicius Veloso de Melo",
  title =        "Studying bloat control and maintenance of effective
                 code in linear genetic programming for symbolic
                 regression",
  journal =      "Neurocomputing",
  volume =       "180",
  pages =        "79--93",
  year =         "2016",
  note =         "Progress in Intelligent Systems Design Selected papers
                 from the 4th Brazilian Conference on Intelligent
                 Systems (BRACIS 2014)",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2015.10.109",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231215015866",
  abstract =     "Linear Genetic Programming (LGP) is an Evolutionary
                 Computation algorithm, inspired in the Genetic
                 Programming (GP) algorithm. Instead of using the
                 standard tree representation of GP, LGP evolves a
                 linear program, which causes a graph-based data flow
                 with code reuse. LGP has been shown to outperform GP in
                 several problems, including Symbolic Regression (SReg),
                 and to produce simpler solutions. In this paper, we
                 propose several LGP variants and compare them with a
                 traditional LGP algorithm on a set of benchmark SReg
                 functions from the literature. The main objectives of
                 the variants were to both control bloat and privilege
                 useful code in the population. Here we evaluate their
                 effects during the evolution process and in the quality
                 of the final solutions. Analysis of the results showed
                 that bloat control and effective code maintenance
                 worked, but they did not guarantee improvement in
                 solution quality.",
  keywords =     "genetic algorithms, genetic programming, Bloat
                 control, Effective code, Symbolic regression",
}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo

Citations