Strategies for Improving the Distribution of Random Function Outputs in GSGP

Created by W.Langdon from gp-bibliography.bib Revision:1.4534

  author =       "Luiz Otavio V. B. Oliveira and Felipe Casadei and 
                 Gisele Pappa",
  title =        "Strategies for Improving the Distribution of Random
                 Function Outputs in GSGP",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "164--177",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-55696-3_11",
  abstract =     "In the last years, different approaches have been
                 proposed to introduce semantic information to genetic
                 programming. In particular, the geometric semantic
                 genetic programming (GSGP) and the interesting
                 properties of its evolutionary operators have gotten
                 the attention of the community. This paper is
                 interested in the use of GSGP to solve symbolic
                 regression problems, where semantics is defined by the
                 output set generated by a given individual when applied
                 to the training cases. In this scenario, both mutation
                 and crossover operators defined with fitness function
                 based on Manhattan distance use randomly built
                 functions to generate offspring. However, the outputs
                 of these random functions are not guaranteed to be
                 uniformly distributed in the semantic space, as the
                 functions are generated considering the syntactic
                 space. We hypothesize that the non-uniformity of the
                 semantics of these functions may bias the search, and
                 propose three different standard normalization
                 techniques to improve the distribution of the outputs
                 of these random functions over the semantic space. The
                 results are compared with a popular strategy that uses
                 a logistic function as a wrapper to the outputs, and
                 show that the strategies tested can improve the results
                 of the previous method. The experimental analysis also
                 indicates that a more uniform distribution of the
                 semantics of these functions does not necessarily imply
                 in better results in terms of test error.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Luiz Otavio Vilas Boas Oliveira Felipe Casadei Gisele L Pappa