{"references": ["J. H. Holland, \"Adaptation in Natural and Artificial Systems\",\nUniversity of Michigan Press, Ann Arbor, 1975.", "K. A. DeJong, An Analysis of the Behavior of a Class of Genetic\nAdaptive Systems, Ph.D. Thesis, University of Michigan, 1975.", "D. E. Goldberg, Genetic Algorithms in Search, Optimization, and\nMachine Learning, Addison-Wesley, Reading, MA., 1989.", "H. Chen, \"Machine learning for information retrieval: neural networks,\nsymbolic learning, and genetic algorithms\". Journal of the American\nSociety for Information Science, 46(3), 1995, pp. 194-216.", "J. Savoy and D. Vrajitoru, \"Evaluation of learning schemes used in\ninformation retrieval (CR-I-95-02)\". Universite de Neuchatel, Faculte de\ndroit et des Sciences Economiques, 1996.", "M. Gordon, \"Probabilistic and genetic algorithms in document\nretrieval\". Communications of the ACM, 31(10), 1988, pp. 1208-1218.", "J. Yang, R. Korfhage and E. Rasmussen. \"Query improvement in\ninformation retrieval using genetic algorithms--a report on the\nexperiments of the TREC project\". In Proceedings of the 1st text\nretrieval conference (TREC-1), 1992, pp. 31-58.", "J. Morgan and A. Kilgour. \"Personalising on-line information retrieval\nsupport with a genetic algorithm\". In A. Moscardini, & P. Smith (Eds.),\nPolyModel 16: Applications of artificial intelligence, 1996, pp. 142-149.", "M. Boughanem, C. Chrisment, and L. Tamine. \"On using genetic\nalgorithms for multimodal relevance optimization in information\nretrieval\". Journal of the American Society for Information Science and\nTechnology, 53(11), 2002, pp. 934-942.\n[10] J. T. Horng and C. C. Yeh. \"Applying genetic algorithms to query\noptimization in document retrieval\". Information Processing &\nManagement, 36(5), 2000, pp. 737-759.\n[11] D. Vrajitoru. \"Crossover improvement for the genetic algorithm in\ninformation retrieval\". Information Processing& Management, 34(4),\n1998, pp. 405-415.\n[12] D. Vrajitoru. \"Large population or many generations for genetic\nalgorithms? Implications in information retrieval\". In F. Crestani and G.\nPasi (Eds.), Soft computing in information retrieval. Techniques and\napplications, Physica-Verlag, 2000, pp. 199-222.\n[13] D. Harman. \"Overview of the first TREC conference\". In Proceedings of\nthe 16th ACM SIGIR conference on information retrieval, 1993, pp. 36-\n47.\n[14] B. T. Bartell, G. W. Cottrell and R. K. Belew. \"Automatic combination\nof multiple ranked retrieval systems\". In Proceedings of the 17th ACM\nSIGIR conference on information retrieval, 1994, pp. 173-181.\n[15] P. Pathak, M. Gordon and W. Fan. \"Effective information retrieval using\ngenetic algorithms based matching functions adaption\", in: Proc. 33rd\nHawaii International Conference on Science (HICS), Hawaii, USA,\n2000.\n[16] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval,\nAdisson, 1999.\n[17] G. Salton and M.H. McGill. Introduction to Modern Information\nRetrieval, McGraw-Hill, 1983.\n[18] C.J. Van Rijsbergen. Information Retrieval, second ed., Butterworth,\n1979.\n[19] A. Bookstein. \"Outline of a general probabilistic retrieval model\",\nJournal of Documentation 39 (2), 1983, pp. 63-72.\n[20] N. Fuhr. \"Probabilistic models in information retrieval\", Computer\nJournal 35 (3), 1992, pp. 243-255.\n[21] C. H. Chang and C. C. Hsu. The design of an information system for\nhypertext retrieval and automatic discovery on WWW. Ph.D. thesis,\nDepartment of CSIE, National Taiwan University, 1999.\n[22] K. L. Kwok. \"Comparing representations in Chinese information\nretrieval\". ACM SIGIR'97, Philadelphia, PA, USA, 1997, pp. 34 -41.\n[23] T. Mitchell. Machine Learning, McGraw-Hill, 1997.\n[24] H. Chen et al., \"A machine learning approach to inductive query by\nexamples: an experiment using relevance feedback, ID3, genetic\nalgorithms, and simulated annealing\", Journal of the American Society\nfor Information Science 49 (8), 1998, pp. 693-705.\n[25] W. Fan, M.D. Gordon and P. Pathak. \"Personalization of search engine\nservices for effective retrieval and knowledge management\", in: Proc.\n2000 International Conference on Information Systems (ICIS), Brisbane,\nAustralia, 2000.\n[26] A.M. Robertson and P. Willet. \"Generation of equifrequent groups of\nwords using a genetic algorithm\", Journal of Documentation 50 (3),\n1994, pp. 213-232.\n[27] M. Gordon. \"Probabilistic and genetic algorithms for document\nretrieval\", Communications of the ACM 31 (10), 1988, pp. 1208-1218.\n[28] W. Fan, M.D. Gordon and P. Pathak. \"Discovery of context-specific\nranking functions for effective information retrieval using genetic\nprogramming\", IEEE Transactions on knowledge and Data Engineering,\nin press.\n[29] M.P. Smith, M. Smith. \"The use of genetic programming to build\nBoolean queries for text retrieval through relevance feedback\", Journal\nof Information Science 23 (6), 1997, pp. 423-431.\n[30] J. Koza. \"Genetic Programming\". On the Programming of Computers by\nmeans of Natural Selection, The MIT Press, 1992.\n[31] J. Yang and R. Korfhage. \"Query modifications using genetic algorithms\nin vector space models\", International Journal of Expert Systems 7 (2),\n1994, pp.165-191.\n[32] H. Kucera and N. Francis. \"Computational analysis of present-day\nAmerican English\". Providence, RD: Brown University Press, 1967.\n[33] M. F. Porter. \"An algorithm for suffix stripping. Program\", 14(3), 1980,\npp. 130-137.\n[34] G. Salton and C. Buckley. \"Improving retrieval performance by\nrelevance feedback\". Journal of the American Society for Information\nScience, 41(4), 1990, pp. 288-297.\n[35] T. Noreault, M. McGill and M. B. Koll. \"A performance evaluation of\nsimilarity measures, document term weighting schemes and\nrepresentation in a Boolean environment\". Information retrieval\nresearch. London: Butterworths, 1981."]}

This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function.