A fuzzy genetic algorithm approach to an adaptive information retrieval agent

We present an approach to a Genetic Information Retrieval Agent Filter (GIR AF) for documents from the Internet using a genetic algorithm (GA) with fuz zy set genes to learn the user's information needs. The population of chrom osomes with fixed length represents such user's preferences. Each chromosom e is associated with a fitness that may be considered the system's belief i n the hypothesis that the chromosome, as a query, represents the user's inf ormation needs. In a chromosome, every gene characterizes documents by a ke yword and an associated occurrence frequency, represented by a certain type of a fuzzy subset of the set of positive integers. Based on the user's eva luation of the documents retrieved by the chromosome, compared to the score s computed by the system, the fitness of the chromosomes is adjusted. A pro totype of GIRAF has been developed and tested. The results of the test are discussed, and some directions for further works are pointed out.