Multiple query evaluation based on an enhanced genetic algorithm |
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Institution: | 1. ISYCOM/GRIMM Université de Toulouse II, 5 Allées A. Machado, 31058 Toulouse Cedex, France;2. IRIT SIG Université de Toulouse III, 118 Route de Narbonne, 31062 Toulouse, France;1. Electronic and Communication Engineering Department, College of Engineering, University of Baghdad, P Box 47202, Baghdad, Iraq;2. Wireless and Photonics Network Research Centre, Faculty of Engineering, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia |
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Abstract: | Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model. |
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