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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Back, T., Fogel, D., Michalewicz, Z.: Handbook of evolutionary computation. Oxford University Press, Oxford (1997)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley, Reading (1999)
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)
Chankong, V., Haimes, Y.Y.: Multiobjective decision making theory and methodology. North-Holland, Amsterdam (1983)
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)
Coello, C.A., Van Veldhuizen, D.A., Lamant, G.B.: Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, Dordrecht (2002)
Cordón, O., et al.: A review on the application of evolutionary computation to information retrieval. International Journal of Approximate Reasoning 34, 241–264 (2003)
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)
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)
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)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
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)
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)
Jaszkiewicz, A.: Genetic local search for mult-objective combinational optimization. European Journal of Operational Research 137, 50–71 (2002)
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)
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)
Korfhage, R., Yang, J.: Query modifications using genetic algorithms in vector space models. International Journal of Expert Systems 7(2), 165–191 (1994)
Koza, J.: Genetic programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
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)
Lozano, M., et al.: Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation 12(3), 272–302 (2004)
Salton, G.: Automatic text processing: The transformation, analysis and retrieval of information by computer. Addison-Wesley, Reading (1989)
Salton, G.: The smart retrieval system – Experiments in automatic document processing. Prentice-Hall, Englewood Cliffs (1997)
Salton, G., McGill, M.J.: An introduction to modern information retrieval. McGraw-Hill, New York (1983)
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)
van Rijsbergen, C.J.: Information retrieval. Butterworth, London (1979)
Waller, W.G., Kraft, D.H.: A mathematical model of a weighted boolean retrieval system. Information Processing and Management 15, 235–245 (1979)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–271 (2000)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
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)