Generalisation and Domain Adaptation in GP with Gradient Descent for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Chen:2015:CEC,
  author =       "Qi Chen and Bing Xue and Mengjie Zhang",
  title =        "Generalisation and Domain Adaptation in GP with
                 Gradient Descent for Symbolic Regression",
  booktitle =    "Proceedings of 2015 IEEE Congress on Evolutionary
                 Computation (CEC 2015)",
  year =         "2015",
  editor =       "Yadahiko Murata",
  pages =        "1137--1144",
  address =      "Sendai, Japan",
  month =        "25-28 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2015.7257017",
  abstract =     "Genetic programming (GP) has been widely applied to
                 symbolic regression problems and achieved good success.
                 Gradient descent has also been used in GP as a
                 complementary search to the genetic beam search to
                 further improve symbolic regression performance.
                 However, most existing GP approaches with gradient
                 descent (GPGD) to symbolic regression have only been
                 tested on the conventional symbolic regression problems
                 such as benchmark function approximations and
                 engineering practical problems with a single (training)
                 data set only and the effectiveness on unseen data sets
                 in the same domain and in different domains has not
                 been fully investigated. This paper designs a series of
                 experiment objectives to investigate the effectiveness
                 and efficiency of GPGD with various settings for a set
                 of symbolic regression problems applied to unseen data
                 in the same domain and adapted to other domains. The
                 results suggest that the existing GPGD method applying
                 gradient descent to all evolved program trees three
                 times at every generation can perform very well on the
                 training set itself, but cannot generalise well on the
                 unseen data set in the same domain and cannot be
                 adapted to unseen data in an extended domain. Applying
                 gradient descent to the best program in the final
                 generation of GP can also improve the performance over
                 the standard GP method and can generalise well on
                 unseen data for some of the tasks in the same domain,
                 but perform poorly on the unseen data in an extended
                 domain. Applying gradient descent to the top 20percent
                 programs in the population can generalise reasonably
                 well on the unseen data in not only the same domain but
                 also in an extended domain.",
  notes =        "1105 hrs 15342 CEC2015",
}

Genetic Programming entries for Qi Chen Bing Xue Mengjie Zhang

Citations