INFERRING NEW DESIGN RULES BY MACHINE LEARNING - A CASE-STUDY OF TOPOLOGICAL OPTIMIZATION

This paper presents a machine learning approach to the topological opt imization of computer networks, Traditionally formulated as an integer program, this problem is well known to be a very difficult one, only solvable by means of heuristic methods. This paper addresses the speci fic problem of inferring new design rules that can reduce the cost of the network, or reduce the message delay below some acceptable thresho ld. More specifically, it extends a recent approach using a rule-based system in order to prevent the risk of combinatorial explosion and to reduce the search space of feasible network topologies. This extensio n essentially implements an efficient inductive learning algorithm lea ding to the refinement of existing rules and to the discovery of new r ules from examples, defined as network topologies satisfying a given r eliability constraint. The contribution of this paper is the integrati on of learning capabilities into topological optimization of computer networks. Computational results confirm the efficiency of the discover ed rules.