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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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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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