A fuzzy genetic algorithm approach to an adaptive information retrieval agent

Abstract

We present an approach to a Genetic Information Retrieval Agent Filter (GIRAF) for documents from the Internet using a genetic algorithm (GA) with fuzzy set genes to learn the user's information needs. The population of chromosomes with fixed length represents such user's preferences. Each chromosome is associated with a fitness that may be considered the system's belief in the hypothesis that the chromosome, as a query, represents the user's information needs. In a chromosome, every gene characterizes documents by a keyword 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 evaluation of the documents retrieved by the chromosome, compared to the scores computed by the system, the fitness of the chromosomes is adjusted. A prototype of GIRAF has been developed and tested. The results of the test are discussed, and some directions for further works are pointed out.

A test of genetic algorithms in relevance feedback

“…Another approach is that of Martín-Bautista and co-workers (Mart ı ın-Bautista, Vila, & Larsen, 1999;Mart ı ın-Bautista, 2000). While this does not follow the vector space model, we find it very interesting in its contribution of new ideas, amongst them two new fitness functions.…”

Genetic algorithms in relevance feedback: a second test and new contributions

“…In recent years, there have appeared numerous applications of ''genetic algorithms'' (GAs) to information retrieval (Belew, 1989;Chen, 1995;Chen, Chung, & Ramsey, 1998;Chen & Iyer, 1998;Cordo on, Moya, & Zarco, 2000Gordon, 1988aGordon, ,b, 1991Horng & Yeh, 2000;Kraft, Petry, Buckles, & Sadasivan, 1994Lo opez-Pujalte, 2000;Lo opez-Pujalte, Guerrero Bote, & Moya Anego on, 2002a,b;Martı ın-Bautista, 2000;Martı ın-Bautista, Vila, & Larsen, 1999;Raghavan & Agarwal, 1987;Raghavan & Birchard, 1979;Robertson & Willett, 1994, 1996Sanchez, 1994;Sanchez, Miyano, & Brachet, 1995;Sanchez & Pierre, 1994;Smith & Smith, 1997;Vrajitoru, 1997Vrajitoru, , 1998Yang & Korfhage, 1992, 1993, 1994. Most of these applications refer to ''relevance feedback'', as is indicated in a review of the topic (Cordo on, Moya, & Zarco, 1999).…”

Fuzzy Methods on the Web: A Critical Discussion

“…Other applications where fuzzy methods have been proposed to manipulate search results are: relevance feedback [21,59,108], meta-search [60], and query expansion [47,58]. The techniques that are applied to this end are as diverse as fuzzy clustering [47], fuzzy association rules [58] and again fuzzy if-then rules [108].…”