Abstract
Interactive evolutionary computation (IEC) is a branch of evolutionary computation where users are involved in the evolution process. In IEC systems the user generally evaluates subjective information of the population in large quantities. One of the problems in the IEC systems is not having friendly interfaces for the evaluation of mass information and this causes the user lose interest. These systems have quickly migrated to the Web by the large number of users that can be found on a voluntary basis. For these applications we can find users with different characteristics, for example, users with different level of knowledge about the application domain, different participation interest or experience in use of Web-Based IEC applications. In this paper we propose a user modeling for IEC to help tailor the user interface depending on the characteristics, preferences, interests, etc. of the user using fuzzy logic.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): Adaptive Web 2007. LNCS, vol. 4321. Springer, Heidelberg (2007)
Frias-Martinez, E., Magoulas, G., Chen, S., Macredie, R.: Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia. Department of Information Systems & Computing Brunel University, Uxbridge, Middlesex. United Kingdom. Rich, E. User Modeling via Stereo-types. Cognitive Science: A Multidisciplinary Journal 3(4), 329–354 (1979b)
Jang, J.-S.R., Sun, C.-T., Mitzutain, E.: Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence (1997) ISBN 0132610663
Kavcic, A.: Fuzzy User Modeling for Adaptation in Educational Hyper-media. In: IEEE 2004 Faculty of Computer and Information Science, 1000. Man, and Cybernetics—Part C: Applications and Reviews, 34(4). University of Ljubljana, Ljubljana (November 2004)
Kowaliw, T., McCormack, J., Dorin, A.: An Iteractive Electronic Art System Based on Artifitial Ecosystemics. Institut Systémes Complexes, Paris, France (2005)
Nguyen, H., Santos Jr., E., Smith, N., Chuett, A.S.: Hybrid User Model for Information Retrieval. National Geospatial Intelligence Agency Grant No. HM158-04-1-2027 and UWW Grant for Undergraduate Research, American Association for Artificial Intelligence (2006), http://www.aaai.org
Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based Modeling for Knowledge Management System. INSEAD, CALT-Centre of Advanced Learning Technologies, 77300, Fontaine bleau, France (2003)
Rich, E.: Building and Exploiting User Models. Unpublished PhD thesis. Carnegie Mellon University, Pittsburgh, PA (1979a)
Rich, E.: User Modeling via Stereotypes. Cognitive Science: A Multidisciplinary Journal 3(4), 329–354 (1979b)
Secretan, J., Beato, N., D’Ambrosio, D.B., Rodriguez, A., Camp-bell, A., Folsom-Kovarik, J.T., Stanley, K.O.: Pic breeder: A Case Study in Collaborative Evolutionary Exploration of Design Space. Department of Electrical Engineering and Computer Science, University of Central Florida. Evolutionary Computation Journal, MIT Press (2011)
Sosnovsky, S.: Ontological Technologies for User Modeling. State-of-the-Art Paper Submitted to the Information Science PhD. Committee of the School of Information Sciences, University of Pittsburgh as Part of Requirements for the Comprehensive Examinations (November 29, 2007)
Takagi, H.: Interactive Evolutionary Computation: Fusion of the capabili-ties of EC Optimization and Human Evaluation. IEEE (2001)
Zadeh, L.A.: Fuzzy sets. Information Control 8, 338–353 (1965)
Zenebe, A., Zhou, L., Norcio, A.F.: User preferences discovery using fuzzy logic. Fuzzy Sets and Systems 161, 3044–3067 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Romero, J.C., García-Valdez, M. (2013). User Modeling for Interactive Evolutionary Computation Applications Using Fuzzy Logic. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-33021-6_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33020-9
Online ISBN: 978-3-642-33021-6
eBook Packages: EngineeringEngineering (R0)