Book Chapter

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.

StatusPublished
Title of seriesEvolutionary Learning and Optimization
Number in series3
Publication date31/12/2009
PublisherSpringer
Publisher URLhttp://link.springer.com/…3-642-12834-9_3#
Place of publicationBerlin Heidelberg
ISSN of series1867-4534
ISBN978-3-642-12833-2