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An Architecture for Evolutionary Adaptive Web Systems

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

Abstract

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.

Keywords

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.

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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

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