Common subtrees in related problems: A novel transfer learning approach for genetic programming

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

@InProceedings{oneill:2017:CEC,
  author =       "Damien O'Neill and Harith Al-Sahaf and Bing Xue and 
                 Mengjie Zhang",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Common subtrees in related problems: A novel transfer
                 learning approach for genetic programming",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "1287--1294",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Transfer learning is a machine learning technique
                 which has demonstrated great success in improving
                 outcomes on a broad range of problems. However prior
                 methods of transfer learning in Genetic Programming
                 (GP) have tended to rely on random processes or
                 meta-knowledge of the problem structure to facilitate
                 selection of information for use in transfer. To
                 address these issues, a non-random method for
                 automatically finding relevant information for transfer
                 between two source domain problems from the same
                 problem domain based on common subtrees is proposed.
                 This information is then used within a modular transfer
                 learning framework, being added to the function set for
                 a target problem prior to population initialisation.
                 The performance of the proposed method is assessed
                 using multiple benchmark problems from two distinct
                 problem domains, namely symbolic regression and Boolean
                 domain problems, and compared to standard GP and
                 the-state-of-the-art transfer learning method for the
                 given problems. The results show that the newly
                 introduced method has either significantly
                 outperformed, or achieved comparable performance to,
                 the competitor methods on the problems of the two
                 domains. We conclude that the proposed method
                 demonstrates ability as a general transfer learning
                 technique for GP and note some possible avenues for
                 future research based off these results.",
  keywords =     "genetic algorithms, genetic programming, Boolean
                 algebra, learning (artificial intelligence), regression
                 analysis, trees (mathematics), Boolean domain problems,
                 common subtrees, machine learning, meta-knowledge,
                 symbolic regression, transfer learning, Algorithm
                 design and analysis, Learning systems, Sociology,
                 Standards, Statistics, Training",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969453",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969453}",
}

Genetic Programming entries for Damien O'Neill Harith Al-Sahaf Bing Xue Mengjie Zhang

Citations