Genetic Approach to Query Space Exploration

• Ankenbrandt C (1990) An extension to the theory of convergence and a proof of the time complexity of genetic algorithms. FOGA90, pp. 53–58.

• Boughanem M and Soule-Dupuy C (1997a) Mercure at trec6. In: Harman DK, ed. 6th International Conference on Text REtrieval TREC6. November 21–23. NIST SP, pp. 321–328.

• Boughanem M and Soule-Dupuy C (1997b) Query modification based on relevance backpropagation. In: Proceedings of the 5th International Conference on Computer-Assisted Information Searching on Internet (RIAO'97), Montreal, pp. 469–487.

• Chang YK, Cirillo GC and Razon J (1971) Evaluation of feedback retrieval using modified freezing, residual collection and test and control groups. In: The Smart Retrieval System: Experiments in Automatic Document Processing, Prentice-Hall Inc., chap. 17, pp. 355–370.

• Chen H (1995) Machine learning for information retrieval: Neural networks, symbolic learning and genetic algorithms. JASIS, 46(3):194–216.

  Google Scholar 

• Davis L (1991) Handbook of Genetic Algorithms. Van Nostram Reinhold, New York.

  Google Scholar 

• Haines D and Croft WB (1993) Relevance feedback and inference networks. In: ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 2–11.

• Goldberg DE (1994) Algorithmes génétiques. Exploration, optimisation et apprentissage automatique. Addison-Wesley, France.

  Google Scholar 

• Gordon M (1988) Probabilistic and genetic algorithms for document retrieval. Communications of the ACM, pp. 1208–1218.

• Grefenstette JJ (1995) Virtual genetic algorithms: First results, Technical report AIC–95–013, Navy Center for Applied Research in Artificial Intelligence.

• Harman D (1997) TREC overview. In: 6th International Conference on Text REtrieval TREC6, November 21–23. Harman DK, ed. NIST SP, pp. 1–24.

• Harman D (1992) Relevance feedback revisited. In: 15th Annual ACM SIGIR Conference On Research and Development in Information Retrieval Copenhague, Denmark, pp. 1–10.

• Holland J (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.

  Google Scholar 

• Holland J (1992) Les algorithmes gënëtiques. Revue POUR LA SCIENCE 179, pp. 44–51.

  Google Scholar 

• Koza JR (1991) A hierarchical approach to learning the Boolean multiplexer function. In: Rawlins G, ed., Foundations of Genetic Algorithms. Morgan Kaufman, San Mateo, CA, pp. 171–192.

  Google Scholar 

• Kraft DH, Petry FE, Buckles BP and Sadisavan T (1995) Applying Genetic Algorithms to Information Retrieval Systems Via Relevance Feedback. In: Bosc and Kacprzyk J, eds. Fuzziness in Database Managment Systems. Studies in Fuzziness Series, Physica-Verlag, Heidelberg, Germany, pp. 330–344.

  Google Scholar 

• Kwok KL (1989) A neural network for probabilistic information retrieval. In: Proceedings of the 12th Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, Cambridge, MA, pp. 21–30.

• Radcliffe NJ (1991) Equivalence class analysis of genetic algorithms. Complex Systems, 5:183–220.

  Google Scholar 

• Robertson S and Sparck Jones K (1976) Relevance weighting of search terms. Journal of the American Society for Information Science, 27:129–146.

  Google Scholar 

• Robertson S and Walker S (1997) On relevance weights with little relevance information. ACM/SIGIR International Conference on Research and development in Information Retrieval, pp. 16–24.

• Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G, ed. The Smart System Experiments in Automatic Document Processing, Prentice-Hall, Inc., Englewood Cliffs, NJ, pp. 313–323.

  Google Scholar 

• Salton G (1970) The SMART Retrieval System. Prentice-Hall, Inc. Englewood Cliffs, NJ.

  Google Scholar 

• Salton G and Buckley C (1990) Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41(4):288–297.

  Google Scholar 

• Sebag M and Schoenauer M (1996) Contrôle d'un algorithme genetique. Revue d'intelligence artificielle, 2/3:389–428.

  Google Scholar 

• Singhal A, Buckley C and Mitra M (1996) Pivoted document length normalisation. In: Conference on Research and Development in Information Retrieval (SIGIR), pp. 21–29.

• Tamine L (1998) Reformulation de requêtes dans les SRI: une approche basée sur la génétique, Master Thesis, University of Tizi-Ouzou.

• Wilkinson R and Hingston P (1991) Using the cosine measure in a neural network for document retrieval. In: ACM/SIGIR International Conference on Research and Development in Information Retrieval.

• Wong SKM, Cai YJ and Yao YY (1993) Computation of term associations by a neural network. In: Conference on Research and Development in Information Retrieval (SIGIR), pp. 107–115.

• Yang JJ and Korfhage R (1993) Query optimization in information retrieval using genetic algorithms. ICGA, 93.

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