Learning to rank using evolutionary computation: immune programming or genetic programming?

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

@InProceedings{conf/cikm/WangML09,
  title =        "Learning to rank using evolutionary computation:
                 immune programming or genetic programming?",
  author =       "Shuaiqiang Wang and Jun Ma and Jiming Liu",
  booktitle =    "Proceedings of the 18th ACM Conference on Information
                 and Knowledge Management, CIKM 2009",
  year =         "2009",
  editor =       "David Wai-Lok Cheung and Il-Yeol Song and 
                 Wesley W. Chu and Xiaohua Hu and Jimmy J. Lin",
  pages =        "1879--1882",
  address =      "Hong Kong",
  month =        nov # " 2-6",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Poster
                 session 6",
  isbn13 =       "978-1-60558-512-3",
  DOI =          "doi:10.1145/1645953.1646254",
  bibdate =      "2009-11-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/cikm/cikm2009.html#WangML09",
  abstract =     "Nowadays ranking function discovery approaches using
                 Evolutionary Computation (EC), especially Genetic
                 Programming (GP), have become an important branch in
                 the Learning to Rank for Information Retrieval (LR4IR)
                 field. Inspired by the GP based learning to rank
                 approaches, we provide a series of generalized
                 definitions and a common framework for the application
                 of EC in learning to rank research. Besides, according
                 to the introduced framework, we propose RankIP, a
                 ranking function discovery approach using Immune
                 Programming (IP). Experimental results demonstrate that
                 RankIP evidently outperforms the baselines.

                 In addition, we study the differences between IP and GP
                 in theory and experiments. Results show that IP is more
                 suitable for LR4IR due to its high diversity.",
}

Genetic Programming entries for Shuaiqiang Wang Jun Ma Jiming Liu

Citations