Describes how techniques from artificial life can be used to evolve a population of personalized information filtering agents. The technique of artificial evolution and the technique of learning from feedback are combined to develop a semi-automated information filtering system which dynamically adapts to the changing interests of the user. Results of a set of experiments are presented in which a small population of information filtering agents was evolved to make a personalized selection of news articles from the USENET newsgroups. The results show that the artificial evolution component of the system is responsible for improving the recall rate of the selected set of articles, while learning from feedback component improves the precision rate.
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