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
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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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In this work we introduce an adaptive genetic algorithm for solving a class of interactive production problems in a dynamical environment. In the interactive production problem, a system continuously generates product instances which should meet the requirements of a market of customers/agents which are unknown to it. The only way for the system to know the evaluation of a product instance is the feedback obtained after delivering it to the customer. In a dynamical environment the domain of the products is changing and the customer/agents are changing their preferences over the time. This scenario is common to many IT services and products which are continuously delivered to a mass of anonymous users. The proposed algorithm employs typical genetic operators in order to optimize the product delivered and to adapt it to the environment feed-back and evolution. Differently from classical GA the goal of such system is to maximize the average result instead of determining the best optimal solution. Experimental results are promising and show interesting properties of the adaptive behavior of GA techniques.