Adaptive Information Filtering using Evolutionary Computation

Information Filtering is concerned with ®ltering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment the ®ltering has to adapt too in order to remain e€ective. Adaptive Information Filtering (AIF) is concerned with ®ltering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). Weighted trigram analysis is a quick and ̄exible technique for describing the contents of a document. A novel application of evolutionary computation is its use in Adaptive Information Filtering for optimizing various parameters, notably the weights associated with trigrams. The research described in this paper combines weighted trigram analysis, clustering, and a special two-pool evolutionary algorithm, to create an Adaptive Information Filtering system with such useful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly. We designed a special evolutionary algorithm with a two-pool strategy for this changing environment. Ó 2000 Elsevier Science Inc. All rights reserved.