Evolutionary computation techniques have been paid great attention as regards their potential as optimization techniques for complex functions. In this paper, we consider three of them: multiple restart hill-climbing, population based incremental learning and genetic algorithms. Their binary version and a real-coded variant of each of these techniques arc experimented on a real problem: traffic supervision in telephone networks. Indeed, this task needs determine streams responsible for call losses in a network by comparing their traffic values to nominal values. However, stream traffic values are not directly available from an on line data acquisition system and, hence, they have to be computed by inverting a simple computational model of stream propagation in circuit-switched networks only based on Erlang's formula equation plus qualitative knowledge about the network.
Evolutionary Computation, Genetic Algorithms, Qqalitative Modeling, Telephone Networks.
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