Plastic Fitness Predictors Coevolved with Cartesian Programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Michal Wiglasz and Michaela Drahosova",
  title =        "Plastic Fitness Predictors Coevolved with Cartesian
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "164--179",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_11",
  abstract =     "Coevolution of fitness predictors, which are a small
                 sample of all training data for a particular task, was
                 successfully used to reduce the computational cost of
                 the design performed by cartesian genetic programming.
                 However, it is necessary to specify the most
                 advantageous number of fitness cases in predictors,
                 which differs from task to task. This paper introduces
                 a new type of directly encoded fitness predictors
                 inspired by the principles of phenotypic plasticity.
                 The size of the coevolved fitness predictor is adapted
                 in response to the learning phase that the program
                 evolution goes through. It is shown in 5 symbolic
                 regression tasks that the proposed algorithm is able to
                 adapt the number of fitness cases in predictors in
                 response to the solved task and the program evolution
  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and

Genetic Programming entries for Michal Wiglasz Michaela Sikulova