Evolutionary learning of Boolean queries by multiobjective genetic programming
Inhaltsverzeichnis
Referenz
O. Cordón, E. Herrera-Viedma, M. Luque: Evolutionary learning of Boolean queries by multiobjective genetic programming. in: Proc. PPSN-VII, Granada, Spain, 2002, pp. 710–719, LNCS 2439
DOI
http://dx.doi.org/10.1007/3-540-45712-7_68
Abstract
The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task.
Extended Abstract
Bibtex
@Inbook{Cordón2002,
author="Cord{\'o}n, Oscar and Herrera-Viedma, Enrique and Luque, Mar{\'i}a",
editor="Guerv{\'o}s, Juan Juli{\'a}n Merelo and Adamidis, Panagiotis and Beyer, Hans-Georg and Schwefel, Hans-Paul and Fern{\'a}ndez-Villaca{\~{n}}as, Jos{\'e}-Luis",
title="Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming",
bookTitle="Parallel Problem Solving from Nature --- PPSN VII: 7th International Conference Granada, Spain, September 7--11, 2002 Proceedings",
year="2002",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="710--719",
isbn="978-3-540-45712-1",
doi="10.1007/3-540-45712-7_68",
url="http://dx.doi.org/10.1007/3-540-45712-7_68 http://de.evo-art.org/index.php?title=Evolutionary_learning_of_Boolean_queries_by_multiobjective_genetic_programming"
}
Used References
1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval, Adisson-Wesley (1999).
2. Baker, J.E.: Reducing bias and ine.ciency in the selection algorithm, Proc. Second International Conference on Genetic Algorithms (ICGA’87), Hillsdale, NJ, (1987) 14–21.
3. Bordogna, G., Carrara, P., Pasi, G.: Fuzzy approaches to extend Boolean information retrieval, in: P. Bosc, J. Kacprzyk Eds., Fuzziness in Database Management Systems (1995) 231–274.
4. Chen, H.: A machine learning approach to inductive query by examples: an experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing, Journal of the American Society for Information Science 49:8 (1998) 693–705.
5. Coello, C.A., Van Veldhuizen, D.A., Lamant, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers (2002).
6. Cordón, O., Moya, F., Zarco, C.: A brief study on the application of genetic algorithms to information retrieval (in spanish), Proc. Fourth International Society for Knowledge Organization (ISKO) Conference (EOCONSID’99), Granada, Spain, (April, 1999) 179–186.
7. 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 (2000) 309–322.MATH
8. Cordón, O., Moya, F., Zarco, C.: A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems, Soft Computing 6:5 (2002).
9. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, Wiley (2001).
10. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, Discussion and Generalization, Proc. Fifth International Conference on Genetic Algorithms (ICGA’93), San Mateo, CA (July, 1993) 416–423.
11. Goldberg, D.E., Richardson, J.: Genetic algorithms with sharing for multimodal function optimization, Proc. Second International Conference on Genetic Algorithms (ICGA’87), Hillsdale, NJ, (1987) 41–49.
12. Koza, J.: Genetic programming. On the programming of computers by means of natural selection, The MIT Press (1992).
13. Kraft, D.H., Petry, F.E., Buckles, B.P., Sadasivan, T.: Genetic algorithms for query optimization in information retrieval: relevance feedback, in: E. Sanchez, T. Shibata, L.A. Zadeh, Genetic Algorithms and Fuzzy Logic Systems, World Scientific (1997) 155–173.
14. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals, Sov. Phys. Dokl. 6 (1966) 705–710.
15. Rodríguez-Vazquez, K., Fonseca, C.M., Fleming, P.J.: Multiobjective genetic programming: A nonlinear system identi.cation application, Late Breaking Papers at the Genetic Programming 1997 Conference, Stanford, CA (July, 1997) 207–212.
16. Salton, G., McGill, M.J.: Introduction to modern information retrieval, McGraw-Hill (1989).
17. 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 (1997) 423–431. http://dx.doi.org/10.1177/016555159702300603
18. van Rijsbergen, C.J.: Information Retrieval (2nd edition), Butterworth (1979).
19. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results, Evolutionary Computations 8:2 (2000) 173–195. http://dx.doi.org/10.1162/106365600568202
Links
Full Text
http://delta.cs.cinvestav.mx/~ccoello/EMOO/cordon02.pdf.gz
