User Modeling for Interactive Evolutionary Computation Applications Using Fuzzy Logic
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.
KeywordsUser modeling interactive evolutionary computation IEC Fuzzy logic
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- 1.Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.): Adaptive Web 2007. LNCS, vol. 4321. Springer, Heidelberg (2007)Google Scholar
- 2.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)Google Scholar
- 3.Jang, J.-S.R., Sun, C.-T., Mitzutain, E.: Neuro-Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence (1997) ISBN 0132610663Google Scholar
- 4.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)Google Scholar
- 5.Kowaliw, T., McCormack, J., Dorin, A.: An Iteractive Electronic Art System Based on Artifitial Ecosystemics. Institut Systémes Complexes, Paris, France (2005)Google Scholar
- 6.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
- 7.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)Google Scholar
- 8.Rich, E.: Building and Exploiting User Models. Unpublished PhD thesis. Carnegie Mellon University, Pittsburgh, PA (1979a)Google Scholar
- 10.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)Google Scholar
- 11.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)Google Scholar
- 12.Takagi, H.: Interactive Evolutionary Computation: Fusion of the capabili-ties of EC Optimization and Human Evaluation. IEEE (2001)Google Scholar