Genetic-based approaches in ranking function discovery and optimization in information retrieval -- A framework

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  author =       "Weiguo Fan and Praveen Pathak and Mi Zhou",
  title =        "Genetic-based approaches in ranking function discovery
                 and optimization in information retrieval -- A
  journal =      "Decision Support Systems",
  volume =       "47",
  number =       "4",
  pages =        "398--407",
  year =         "2009",
  note =         "Smart Business Networks: Concepts and Empirical
  ISSN =         "0167-9236",
  DOI =          "doi:10.1016/j.dss.2009.04.005",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Information
                 retrieval, Artificial intelligence, Evolutionary
                 computations, Data fusion",
  abstract =     "An Information Retrieval (IR) system consists of
                 document collection, queries issued by users, and the
                 matching/ranking functions used to rank documents in
                 the predicted order of relevance for a given query. A
                 variety of ranking functions have been used in the
                 literature. But studies show that these functions do
                 not perform consistently well across different
                 contexts. In this paper we propose a two-stage
                 integrated framework for discovering and optimising
                 ranking functions used in IR. The first stage,
                 discovery process, is accomplished by intelligently
                 leveraging the structural and statistical information
                 available in HTML documents by using Genetic
                 Programming techniques to yield novel ranking
                 functions. In the second stage, the optimization
                 process, document retrieval scores of various
                 well-known ranking functions are combined using Genetic
                 Algorithms. The overall discovery and optimization
                 framework is tested on the well-known TREC collection
                 of web documents for both the ad-hoc retrieval task and
                 the routing task. Using our framework we observe a
                 significant increase in retrieval performance compared
                 to some of the well-known stand alone ranking

Genetic Programming entries for Weiguo Fan Praveen Pathak Mi Zhou