Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm

Citation

Brownlee A, McCall J, Shakya SK & Zhang Q (2009) Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm. In: Chen Y (ed.) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, 3. Berlin Heidelberg: Springer, pp. 45-69. http://link.springer.com/chapter/10.1007/978-3-642-12834-9_3#; https://doi.org/10.1007/978-3-642-12834-9_3

Abstract
Linkage learning has been a focus of research interest since the early days of evolutionary computation. There is a strong connection between linkage learning and the concept of structure learning, which is a crucial component of a multivariate Estimation of Distribution Algorithm. Structure learning determines the interactions between variables in the probabilistic model of an EDA, based on analysis of the fitness function or a population. In this chapter we apply three different approaches to structure learning in an EDA based on Markov networks and use measures from the information retrieval community (precision, recall and the F-measure) to assess the quality of the structures learned. We present observations and analysis of the impact that structure learning has on optimisation performance and fitness modelling.