Improving Generalization Ability of Genetic Programming: Comparative Study

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Misc{Naik:2013:misc,
  title =        "Improving Generalization Ability of Genetic
                 Programming: Comparative Study",
  author =       "Tejashvi R. Naik and Vipul K. Dabhi",
  howpublished = "arXiv",
  year =         "2013",
  month =        "13 " # apr,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://arxiv.org/abs/1304.3779",
  bibdate =      "2013-05-02",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/corr/corr1304.html#abs-1304-3779",
  size =         "15 pages",
  abstract =     "In the field of empirical modelling using Genetic
                 Programming (GP), it is important to evolve solution
                 with good generalisation ability. Generalisation
                 ability of GP solutions get affected by two important
                 issues: bloat and over-fitting. Bloat is uncontrolled
                 growth of code without any gain in fitness and
                 important issue in GP. We surveyed and classified
                 existing literature related to different techniques
                 used by GP research community to deal with the issue of
                 bloat. Moreover, the classifications of different bloat
                 control approaches and measures for bloat are
                 discussed. Next, we tested four bloat control methods:
                 Tarpeian, double tournament, lexicographic parsimony
                 pressure with direct bucketing and ratio bucketing on
                 six different problems and identified where each bloat
                 control method performs well on per problem basis.
                 Based on the analysis of each method, we combined two
                 methods: double tournament (selection method) and
                 Tarpeian method (works before evaluation) to avoid
                 bloated solutions and compared with the results
                 obtained from individual performance of double
                 tournament method. It was found that the results were
                 improved with this combination of two methods.",
}

Genetic Programming entries for Tejashvi R Naik Vipul K Dabhi

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