Telephone network traffic overloading diagnosis and evolutionary computation techniques

Abstract

Traffic supervision in telephone networks is a task which needs to determine streams responsible for call losses in a network by comparing their traffic values to nominal values. However, stream traffic values are not measured by the on-line data acquisition system and, hence, have to be computed. We perform this computation by inverting an approximate knowledge based model of stream propagation in circuit-switched networks. This inversion is computed thanks to three evolutionary computation techniques (multiple restart hill-climbing, population-based incremental learning and genetic algorithms) for which both a binary version and a real variant have been experimented with several fitness measures. The final results first point out how the fitness measure choice can impact on their quality. They also show that, in this case, real variants of the algorithms give significantly better results than binary ones.