On using genetic algorithms for multimodal relevance optimisation in information retrieval

Mohand Boughanem (1, 2) , Claude Chrisment (1) , Lynda Tamine (3)

1 IRIT-SIG - Systèmes d’Informations Généralisées
2 UT3 - Université Toulouse III - Paul Sabatier
3 IRIT-IRIS - Recherche d’Information et Synthèse d’Information

This paper presents a genetic relevance optimisation 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 in order to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC sub-collection validates our approach.

Domaines: Recherche d'information [cs.IR]

Mohand Boughanem, Claude Chrisment, Lynda Tamine. On using genetic algorithms for multimodal relevance optimisation in information retrieval. Journal of the American Society for Information Systems and Technology, 2002, 53 (11), pp.934-942. ⟨10.1002/asi.10119⟩. ⟨hal-00359529⟩