neat Genetic Programming: Controlling Bloat Naturally

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

  author =       "Leonardo Trujillo and Luis Munoz and 
                 Edgar Galvan-Lopez and Sara Silva",
  title =        "neat Genetic Programming: Controlling Bloat
  journal =      "Information Sciences",
  year =         "2016",
  volume =       "333",
  pages =        "21--43",
  month =        "10 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-0255",
  URL =          "",
  DOI =          "doi:10.1016/j.ins.2015.11.010",
  size =         "23",
  abstract =     "Bloat is one of the most widely studied phenomena in
                 Genetic Programming (GP), it is normally defined as the
                 increase in mean program size without a corresponding
                 improvement in fitness. Several theories have been
                 proposed in the specialized GP literature that explain
                 why bloat occurs. In particular, the Crossover-Bias
                 Theory states that the cause of bloat is that the
                 distribution of program sizes during evolution is
                 skewed in a way that encourages bloat to appear, by
                 punishing small individuals and favouring larger ones.
                 Therefore, several bloat control methods have been
                 proposed that attempt to explicitly control the size
                 distribution of programs within the evolving
                 population. This work proposes a new bloat control
                 method called neat-GP, that implicitly shapes the
                 program size distribution during a GP run. neat-GP is
                 based on two key elements: (a) the NeuroEvolution of
                 Augmenting Topologies algorithm (NEAT), a robust
                 heuristic that was originally developed to evolve
                 neural networks; and (b) the Flat Operator Equalization
                 bloat control method, that explicitly shapes the
                 program size distributions toward a uniform or flat
                 shape. Experimental results are encouraging in two
                 domains, symbolic regression and classification of
                 real-world data. neat-GP can curtail the effects of
                 bloat without sacrificing performance, outperforming
                 both standard GP and the Flat-OE method, without
                 incurring in the computational overhead reported by
                 some state-of-the-art bloat control methods",
  notes =        "Presentation \cite{Trujillo:2016:GECCOcomp}",

Genetic Programming entries for Leonardo Trujillo Luis Munoz Delgado Edgar Galvan Lopez Sara Silva