User models in dialog systems
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Optimizing Web Page Layout Using an Annealed Genetic Algorithm as Client-Side Script
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An architecture for evolutionary adaptive web systems
WINE'05 Proceedings of the First international conference on Internet and Network Economics
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An evolutionary adaptive algorithm for solving a class of online service provider problems in a dynamical web environment is introduced. In the online service provider scenario, a system continuously generates digital products and service instances by assembling components (e.g. headlines of online newspapers, search engine query results, advertising lists) to fulfill the requirements of a market of anonymous customers. The evaluation of a service instance can only be known by the feedback obtained after delivering it to the customer over the internet or through telephone networks. In dynamic domains available components and customer/agents preferences are changing over the time. The proposed algorithm employs typical genetic operators in order to optimize the service delivered and to adapt it to the environment feedback and evolution. Differently from classical genetic algorithms the goal of such systems is to maximize the average fitness instead of determining the single best optimal service/product. Experimental results for different classes of services, online newspapers and search engines, confirm the adaptive behavior of the proposed technique.