Applying Heuristics to Improve A Genetic Query Optimisation Process in Information Retrieval

This work presents a genetic approach for query optimisation in information retrieval. The proposed GA is improved y heuristics in order to solve the relevance multimodality problem and adapt the genetic exploration process to the information retrieval task. Experiments with AP documents and queries issued from TREC show the effectiveness of our GA model

[1]  John R. Koza,et al.  A Hierarchical Approach to Learning the Boolean Multiplexer Function , 1990, FOGA.

[2]  W. Bruce Croft,et al.  Relevance feedback and inference networks , 1993, SIGIR.

[3]  Donna K. Harman,et al.  Relevance feedback revisited , 1992, SIGIR '92.

[4]  Stephen E. Robertson,et al.  On relevance weights with little relevance information , 1997, SIGIR '97.

[5]  Michael D. Gordon User-based document clustering by redescribing subject descriptions with a genetic algorithm , 1991, J. Am. Soc. Inf. Sci..

[6]  Mohand Boughanem,et al.  Query optimisation using an improved genetic algorithm , 2000, CIKM '00.

[7]  Michael D. Gordon Probabilistic and genetic algorithms in document retrieval , 1988, CACM.

[8]  Gerard Salton,et al.  Improving Retrieval Performance by Relevance Feedback , 1997 .

[9]  S. Hassas Les algorithmes génétiques , 1996 .

[10]  Donald H. Kraft,et al.  Applying Genetic Algorithms to Information Retrieval Systems Via Relevance Feedback , 1995 .

[11]  Mohand Boughanem,et al.  Query modification based on relevance backpropagation , 1997, RIAO.

[12]  Carol A. Ankenbrandt An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms , 1990, FOGA.

[13]  Hsinchun Chen,et al.  Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms , 1995, J. Am. Soc. Inf. Sci..

[14]  Kui-Lam Kwok,et al.  A network approach to probabilistic information retrieval , 1995, TOIS.