The way of adjusting parameters of the expert system shell McESE: New approach

We have designed and developed a general knowledge representation tool, an expert system shell called McESE (McMaster Expert System Environment); it derives a set of production (decision) rules of a very general form. Such a production set can be equivalently symbolized as a decision tree. McESE exhibits several parameters such as the weights, thresholds, and the certainty propagation functions that have to be adjusted (designed) according to a given problem, for instance, by a given set of training examples. We can use the traditional machine learning (ML) or data mining (DM) algorithms for inducing the above parameters can be utilized. In this methodological case study, we discuss an application of genetic algorithms (GAs) to adjust (generate) parameters of the given tree that can be then used in the rule-based expert system shell McESE. The only requirement is that a set of McESE decision rules (or more precisely, the topology of a decision tree) be given.