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Network administration is a task that requires experience in relating symptoms of network problems with possible causes and corrective actions. We describe the design of a system and more specifically its information retrieval component, which aims to retrieve articles relevant to a given problem case from a collection of articles describing previously solved problems and their associated solutions. An article is described by a term vector. We present a methodology for defining the vocabulary and preliminary results for assessing the quality of expert-proposed modifications to the vocabulary. We obtain vocabulary-derived document classes from a selforganising map and assess vocabulary quality using the quality of classification into these classes.

Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited in previous work. Here, we revisit the design of fitness functions for genetic programming by explicitly considering the contribution of the wrapper and cost function. Within the context of supervised learning, as applied to classification problems, a clustering methodology is introduced using cost functions which encourage maximization of separation between in and out of class exemplars. Through a series of empirical investigations of the nature of these functions, we demonstrate that classifier performance is much more dependable than previously the case under the genetic programming paradigm.

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