MOA - Markovian optimisation algorithm

Siddhartha Shakya, Roberto Santana

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

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.

Original languageBritish English
Title of host publicationMarkov Networks in Evolutionary Computation
PublisherSpringer Verlag
Pages39-53
Number of pages15
Edition1
ISBN (Print)9783642288999
DOIs
StatePublished - 2012

Publication series

NameAdaptation, Learning, and Optimization
Number1
Volume14
ISSN (Print)1867-4534
ISSN (Electronic)1867-4542

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