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Multi-objective Evolutionary Algorithms in the Automatic Learning of Boolean Queries: A Comparative Study

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Theoretical Advances and Applications of Fuzzy Logic and Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 42))

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Abstract

The performance of Information Retrieval Systems (IRSs) is usually measured using two different criteria, precision and recall. In such a way, the problem of tuning an IRS may be considered as a multi-objective optimization problem. In this contribution, we focus on the automatic learning of Boolean queries in IRSs by means of multi-objective evolutionary techniques. We present a comparative study of four multi-objective evolutionary optimization techniques of general-purpose (NSGA-II, SPEA2 and two MOGLS) to learn Boolean queries.

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References

  1. Back, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. Oxford University Press, Oxford (1997)

    Google Scholar 

  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley, Reading (1999)

    Google Scholar 

  3. Bordogna, G., Carrara, P., Pasi, G.: Fuzzy Approaches to Extend Boolean Information Retrieval. In: Bosc, P., Kacpryk, J. (eds.) Fuzzy sets and possibility theory in database management systems, pp. 231–274. Springer, Heidelberg (1995)

    Google Scholar 

  4. Chankong, V., Haimes, Y.Y.: Multiobjective decision making theory and methodology. North-Holland, Amsterdam (1983)

    MATH  Google Scholar 

  5. Chen, H., et al.: A machine learning approach to inductive query by example: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing. Journal of the American Society for Information Science 49(8), 693–705 (1998)

    Article  Google Scholar 

  6. Coello, C.A., Van Veldhuizen, D.A., Lamant, G.B.: Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  7. Cordón, O., et al.: A review on the application of evolutionary computation to information retrieval. International Journal of Approximate Reasoning 34, 241–264 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  8. Cordon, O., Herrera-Viedma, E., Luque, M.: Evolutionary learning of boolean queries by multiobjective genetic programming. In: Guervós, J.J.M., et al. (eds.) Parallel Problem Solving from Nature - PPSN VII. LNCS, vol. 2439, pp. 710–719. Springer, Heidelberg (2002)

    Google Scholar 

  9. Cordón, O., Herrera-Viedma, E., Luque, M.: Improving the learning of boolean queries by means a multiobjective IQBE evolutionary algorithm. Information Processing & Management 42(3), 615–632 (2006)

    Article  Google Scholar 

  10. Cordón, O., Moya, F., Zarco, C.: A GA-P algorithm to automatically formulate extended boolean queries for a fuzzy information retrieval system. Mathware & Soft Computing 7(2-3), 309–322 (2000)

    MATH  Google Scholar 

  11. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  12. Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)

    Article  Google Scholar 

  13. Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man and Cibernetics, Part C: Applications and Reviews 28(3), 392–403 (1998)

    Article  Google Scholar 

  14. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 7(2), 204–223 (2003)

    Article  Google Scholar 

  15. Jaszkiewicz, A.: Genetic local search for mult-objective combinational optimization. European Journal of Operational Research 137, 50–71 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  16. Jaszkiewicz, A.: Do multiple-objective metaheuristics deliver on their promises? A computacional experiment on the set-covering problem. IEEE Transactions on Evolutionary Computation 7(2), 133–143 (2003)

    Article  MathSciNet  Google Scholar 

  17. Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers, 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland, Feb., revised version (2006)

    Google Scholar 

  18. Korfhage, R., Yang, J.: Query modifications using genetic algorithms in vector space models. International Journal of Expert Systems 7(2), 165–191 (1994)

    Google Scholar 

  19. Koza, J.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  20. Kraft, D.H., et al.: Genetic algorithms for query optimization in information retrieval: relevance feedback. In: Sanchez, E., Shibata, T., Zadeh, L.A. (eds.) Genetic algorithms and fuzzy logic systems, pp. 155–173. World Scientific, Singapore (1997)

    Google Scholar 

  21. Lozano, M., et al.: Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation 12(3), 272–302 (2004)

    Article  Google Scholar 

  22. Salton, G.: Automatic text processing: The transformation, analysis and retrieval of information by computer. Addison-Wesley, Reading (1989)

    Google Scholar 

  23. Salton, G.: The smart retrieval system – Experiments in automatic document processing. Prentice-Hall, Englewood Cliffs (1997)

    Google Scholar 

  24. Salton, G., McGill, M.J.: An introduction to modern information retrieval. McGraw-Hill, New York (1983)

    Google Scholar 

  25. Smith, M.P., Smith, M.: The use of genetic programming to build boolean queries for text retrieval through relevance feedback. Journal of Information Science 23(6), 423–431 (1997)

    Article  Google Scholar 

  26. van Rijsbergen, C.J.: Information retrieval. Butterworth, London (1979)

    Google Scholar 

  27. Waller, W.G., Kraft, D.H.: A mathematical model of a weighted boolean retrieval system. Information Processing and Management 15, 235–245 (1979)

    Article  MATH  Google Scholar 

  28. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–271 (2000)

    Article  Google Scholar 

  29. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), pp. 95–100 (2002)

    Google Scholar 

  30. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

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Oscar Castillo Patricia Melin Oscar Montiel Ross Roberto Sepúlveda Cruz Witold Pedrycz Janusz Kacprzyk

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Lopez-Herrera, A.G., Herrera-Viedma, E., Herrera, F., Porcel, C., Alonso, S. (2007). Multi-objective Evolutionary Algorithms in the Automatic Learning of Boolean Queries: A Comparative Study. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_8

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  • DOI: https://doi.org/10.1007/978-3-540-72434-6_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72433-9

  • Online ISBN: 978-3-540-72434-6

  • eBook Packages: EngineeringEngineering (R0)

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