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

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Exploitation of Linkage Learning in Evolutionary Algorithms

Part of the book series: Evolutionary Learning and Optimization ((ALO,volume 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.

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Author information

Authors and Affiliations

  1. IDEAS Research Institute, Robert Gordon University, Aberdeen, UK

    Alexander E. I. Brownlee & John A. W. McCall

  2. Intelligent Systems Research Centre, BT Group, Ipswich, UK

    Siddhartha K. Shakya

  3. Department of Computer Science, University of Essex, Colchester, Essex, UK

    Qingfu Zhang

Authors
  1. Alexander E. I. Brownlee
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  2. John A. W. McCall
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  3. Siddhartha K. Shakya
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  4. Qingfu Zhang
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Editor information

Editors and Affiliations

  1. Natural Computing Laboratory Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, 300, HsinChu City, Taiwan

    Ying-ping Chen

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Brownlee, A.E.I., McCall, J.A.W., Shakya, S.K., Zhang, Q. (2010). Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_3

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  • DOI: https://doi.org/10.1007/978-3-642-12834-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12833-2

  • Online ISBN: 978-3-642-12834-9

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