An Architecture for Evolutionary Adaptive Web Systems
This paper present an architecture based on evolutionary genetic algorithms for generating online adaptive services. Online adaptive systems provide flexible services to a mass of clients/users for maximising some system goals, they dynamically adapt the form and the content of the issued services while the population of clients evolve over time. The idea of online genetic algorithms (online GAs) is to use the online clients response behaviour as a fitness function in order to produce the next generation of services. The principle implemented in online GAs, “the application environment is the fitness”, allow to model highly evolutionary domains where both services providers and clients change and evolve over time. The flexibility and the adaptive behaviour of this approach seems to be very relevant and promising for applications characterised by highly dynamical features such as in the web domain (online newspapers, e-markets, websites and advertising engines). Nevertheless the proposed technique has a more general aim for application environments characterised by a massive number of anonymous clients/users which require personalised services, such as in the case of many new IT applications.
KeywordsGenetic Algorithm Fitness Function Anonymous User Online Newspaper Adaptive Service
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- 2.Zadeh, L.A.: Fuzzy Sets Information and Control 8(3), 338–353 (1965)Google Scholar
- 4.Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Machine Learning (1997)Google Scholar
- 5.Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Michigan (1975)Google Scholar
- 6.Whitley, D.: An overview of evolutionary algorithms. Information and Software Technology (2001)Google Scholar
- 8.Hagen, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Co., Boston (1996)Google Scholar
- 9.Masui, T.: Graphic object layout with interactive genetic algorithms. In: Proc. IEEE Visual Languages 1992 (1992)Google Scholar
- 11.Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. In: Proceedings of the IEEE 2001. IEEE Press, Los Alamitos (2001)Google Scholar
- 13.Becker, L.A., Seshadri, M.: GP-evolved Technical Trading Rules Can Outperform Buy and Hold. In: 3rd International Workshop on Computational Intelligence in Economics and Finance (September 2003)Google Scholar
- 14.Kay, J., Kummerfeld, B., Lauder, P.: Managing private user models and shared personas. In: Proceedings of Workshop on User Modelling for Ubiquitous Computing, User Modeling (2003)Google Scholar
- 17.Koutri, M., Daskalaki, S., Avouris, N.: Adaptive Interaction with Web Sites: an Overview of Methods and Techniques. In: Proc. of the 4th Int. Workshop on Computer Science and Information technologies CSIT 2002, Patras, Greece (2002)Google Scholar
- 18.Oliver, N., Monmarché, G., Venturini, I.: Interactive design of web sites with a genetic algorithm. In: Proceedings of the IADIS International Conference WWW/Internet, Lisbon, pp. 355–362 (2002)Google Scholar
- 19.González, J., Merelo, J.J., Castillo, P.A., Rivas, V., Romero, G., Prieto, A.: Optimized web newspaper layout using simulated annealing. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1606. Springer, Heidelberg (1999)Google Scholar