Knowledge Acquisition including Tags in a Classifier System

One of the major problems related to Classifier Systems is the loss of rules. This loss is caused by the Genetic Algorithm being applied on the entire population of rules jointly. Obviously, the genetic operators discriminate rules by the strength value, such that evolution favours the generation of the stronger rules. When the learning system works in an environment in which i t is possible to generate a complete training set, the strength of the rules of the CS will reflect the relative relationship between rules satisfactorily and, therefore, the application of the Genetic Algorithm will produce the desired effects. However, when the learning process presents individual cases and allows the system to learn gradually from these cases, each learning interval with a set of individual cases can lead the strength to be distributed in favour of a given type of rules that would in turn be favoured by the Genetic Algorithm. Basically, the idea is to divide rules into groups such that they are forced to remain in the system. This contribution is a method of learning that allows similar knowledge to be grouped. A field in which knowledge-based systems researchers have done a lot of work is concept classification and the relationships that are established between these concepts in the stage of knowledge conceptualization for later formalization. This job of classifying and searching relationships is performed in the proposed Classifier Systems by means of a mechanism, Tags, that allows the classification and the relationships to be discovered without the need for expert knowledge.