On using genetic algorithms for multimodal relevance optimization in information retrieval

  • Authors:
  • M. Boughanem;C. Chrisment;L. Tamine

  • Affiliations:
  • IRIT SIG Université de Toulouse III, 118 Route de Narbonne, 31062 Toulouse, France;IRIT SIG Université de Toulouse III, 118 Route de Narbonne, 31062 Toulouse, France;GRIMM/ISYCOM Université de Toulouse II, 5 Allées A. Machado, 31058 Toulouse Cedex, France

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2002

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Abstract

This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.