Network measures for information extraction in evolutionary algorithms

Problem domain information extraction is a critical issue in many real-world optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classifying different problem instances and predicting the algorithm behavior.