A Cooperative Coevolution Framework for Parallel Learning to Rank

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

  author =       "Shuaiqiang Wang and Yun Wu and Byron J. Gao and 
                 Ke Wang and Hady W. Lauw and Jun Ma",
  title =        "A Cooperative Coevolution Framework for Parallel
                 Learning to Rank",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  year =         "2015",
  volume =       "27",
  number =       "12",
  pages =        "3152--3165",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Cooperative
                 coevolution, learning to rank, information retrieval,
                 immune programming",
  ISSN =         "1041-4347",
  DOI =          "doi:10.1109/TKDE.2015.2453952",
  size =         "14 pages",
  abstract =     "We propose CCRank, the first parallel framework for
                 learning to rank based on evolutionary algorithms (EA),
                 aiming to significantly improve learning efficiency
                 while maintaining accuracy. CCRank is based on
                 cooperative coevolution (CC), a divide-and-conquer
                 framework that has demonstrated high promise in
                 function optimization for problems with large search
                 space and complex structures. Moreover, CC naturally
                 allows parallelization of sub-solutions to the
                 decomposed sub-problems, which can substantially boost
                 learning efficiency. With CCRank, we investigate
                 parallel CC in the context of learning to rank. We
                 implement CCRank with three EA-based learning to rank
                 algorithms for demonstration. Extensive experiments on
                 benchmark datasets in comparison with the
                 state-of-the-art algorithms show the performance gains
                 of CCRank in efficiency and accuracy.",
  notes =        "Also known as \cite{7152946}",

Genetic Programming entries for Shuaiqiang Wang Yun Wu Byron J Gao Ke Wang Hady W Lauw Jun Ma