An Architecture for Evolutionary Adaptive Web Systems

  • Alfredo Milani
  • Stefano Marcugini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3828)


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.


Genetic Algorithm Fitness Function Anonymous User Online Newspaper Adaptive Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kobsa, A., Wahlster, W. (eds.): User Models in Dialog Systems. Springer, London (1989)zbMATHGoogle Scholar
  2. 2.
    Zadeh, L.A.: Fuzzy Sets Information and Control 8(3), 338–353 (1965)Google Scholar
  3. 3.
    Monfared, M.A.S., Steiner, S.J.: Fuzzy adaptive scheduling and control systems. Fuzzy Sets and Systems 115(2), 231–246 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Machine Learning (1997)Google Scholar
  5. 5.
    Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Michigan (1975)Google Scholar
  6. 6.
    Whitley, D.: An overview of evolutionary algorithms. Information and Software Technology (2001)Google Scholar
  7. 7.
    Anderson, J.A.: An Introduction to Neural Networks. MIT Press, Boston (1995)zbMATHGoogle Scholar
  8. 8.
    Hagen, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Co., Boston (1996)Google Scholar
  9. 9.
    Masui, T.: Graphic object layout with interactive genetic algorithms. In: Proc. IEEE Visual Languages 1992 (1992)Google Scholar
  10. 10.
    Peñalver, J.G., Merelo, J.J.: Optimizing web page layout using an annealed genetic algorithm as client-side script. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, p. 1018. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 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
  12. 12.
    Dorigo, M., Schnepf, U.: Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis. IEEE Transactions on Systems, Man and Cybernetics 23(1), 141–154 (1993)CrossRefGoogle Scholar
  13. 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. 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
  15. 15.
    Reiter, M.K., Rubin, A.D.: Crowds: anonymity for Web transactions. ACM Transactions on Information and System Security 1(1), 66–92 (1998)CrossRefGoogle Scholar
  16. 16.
    Kushmerick, N., McKee, J., Toolan, F.: Towards zero-input personalization: Referrer-based page prediction. In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) AH 2000. LNCS, vol. 1892, p. 133. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 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. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alfredo Milani
    • 1
  • Stefano Marcugini
    • 1
  1. 1.Dipartimento di Matematica e InformaticaUniversità di PerugiaPerugiaItaly

Personalised recommendations