Further Investigation on Genetic Programming with Transfer Learning for Symbolic Regression

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

@InProceedings{Haslam:2016:CEC,
  author =       "Edward Haslam and Bing Xue and Mengjie Zhang",
  title =        "Further Investigation on Genetic Programming with
                 Transfer Learning for Symbolic Regression",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3598--3605",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744245",
  abstract =     "Transfer learning is an important approach in machine
                 learning, which aims to solve a problem by using the
                 knowledge learnt from another problem domain. There has
                 been extensive research and great achievement on
                 transfer learning for image analysis and other tasks,
                 but research on transfer learning in genetic
                 programming (GP) for symbolic regression is still in
                 the very early stage. However, GP has a natural way of
                 expressing knowledge by trees or subtrees, which can be
                 automatically discovered during the evolutionary
                 process. An initial work on GP with transfer learning
                 was proposed to transfer knowledge through best trees
                 or subtrees from to source domain to facilitate the
                 learning in the target domain. However, there are still
                 a number of important issues remaining not
                 investigated. This paper further investigates the
                 ability of GP with transfer learning on different types
                 of transfer scenarios, investigates the influence of a
                 key parameter and the effect of transfer learning on
                 the evolutionary training process, and also analyses
                 how the knowledge learnt from the source domain was
                 used during the learning process on the target domain.
                 The results show that GP with transfer learning can
                 generally perform well on different types of transfer
                 scenarios. The transferred knowledge can provide a good
                 initial population for the GP learning on the target
                 domain, speed up the convergence, and help obtain
                 better final solutions. However, the benefits of
                 transfer learning varies in different scenarios.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Edward Haslam Bing Xue Mengjie Zhang

Citations