Generation of simple structured Information Retrieval functions by genetic algorithm without stagnation

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Kulunchakov A. S. and Strijov V. V.",
  title =        "Generation of simple structured Information Retrieval
                 functions by genetic algorithm without stagnation",
  journal =      "Expert Systems with Applications",
  year =         "2017",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2017.05.019",
  URL =          "",
  abstract =     "This paper investigates an approach to construct new
                 ranking models for Information Retrieval. The IR
                 ranking model depends on the document description. It
                 includes the term frequency and document frequency. The
                 model ranks documents upon a user request. The quality
                 of the model is defined by the difference between the
                 documents, which experts assess as relative to the
                 request, and the ranked ones. To boost the model
                 quality a modified genetic algorithm was developed. It
                 generates models as superpositions of primitive
                 functions and selects the best according to the quality
                 criterion. The main impact of the research if the new
                 technique to avoid stagnation and to control structural
                 complexity of the consequently generated models. To
                 solve problems of stagnation and complexity, a new
                 criterion of model selection was introduced. It uses
                 structural metric and penalty functions, which are
                 defined in space of generated superpositions. To show
                 that the newly discovered models outperform the other
                 state-of-the-art IR scoring models the authors perform
                 a computational experiment on TREC datasets. It shows
                 that the resulted algorithm is significantly faster
                 than the exhaustive one. It constructs better ranking
                 models according to the MAP criterion. The obtained
                 models are much simpler than the models, which were
                 constructed with alternative approaches. The proposed
                 technique is significant for developing the information
                 retrieval systems based on expert assessments of the
                 query-document relevance.",
  keywords =     "genetic algorithms, genetic programming, Information
                 retrieval, Ranking function, Evolutionary stagnation,

Genetic Programming entries for Andrey Kulunchakov Vadim Strijov