Network measures for information extraction in evolutionary algorithms
- DOI
- 10.1080/18756891.2013.823004How to use a DOI?
- Keywords
- Knowledge extraction, network theory, optimization, evolutionary algorithms, computational intelligence
- Abstract
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.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Roberto Santana AU - Rubén Armañanzas AU - Concha Bielza AU - Pedro Larrañaga PY - 2013 DA - 2013/11/01 TI - Network measures for information extraction in evolutionary algorithms JO - International Journal of Computational Intelligence Systems SP - 1163 EP - 1188 VL - 6 IS - 6 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.823004 DO - 10.1080/18756891.2013.823004 ID - Santana2013 ER -