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MOA - Markovian Optimisation Algorithm

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Markov Networks in Evolutionary Computation

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 14))

Abstract

In this chapter we describe Markovian Optimisation Algorithm (MOA), one of the recent developments in MN based EDA. It uses the local Markov property to model the dependency and directly sample from it without needing to approximate a complex join probability distribution model. MOA has a much simpler workflow in comparison to its global property based counter parts, since expensive processes to finding cliques, and building and estimating clique potential functions are avoided. The chapter is intended as an introductory chapter, and describes the motivation and the workflow of MOA. It also reviews some of the results obtained with it.

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Author information

Authors and Affiliations

  1. Business Modelling and Operational Transformation Practice, BT Innovate & Design, Ipswich, UK

    Siddhartha Shakya

  2. Intelligent Systems Group. Faculty of Informatics, University of the Basque Country (UPV/EHU), San-Sebastian, Spain

    Roberto Santana

Authors
  1. Siddhartha Shakya
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  2. Roberto Santana
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Corresponding author

Correspondence to Siddhartha Shakya .

Editor information

Editors and Affiliations

  1. Transformation Practice, BT Innovate & Design, Business Modelling and Operational, Cauldwell Hall Road 172, Ipswich, IP4 5DB, United Kingdom

    Siddhartha Shakya

  2. Intelligent Systems Group, Faculty of Informatics, University of the Basque Country, Boadilla del Monte, San Sebastian, Spain

    Roberto Santana

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Shakya, S., Santana, R. (2012). MOA - Markovian Optimisation Algorithm. In: Shakya, S., Santana, R. (eds) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28900-2_3

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  • DOI: https://doi.org/10.1007/978-3-642-28900-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28899-9

  • Online ISBN: 978-3-642-28900-2

  • eBook Packages: EngineeringEngineering (R0)

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