Markov modelling and parameterisation of genetic evolutionary test generations

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dc.contributor.authorCheng, A.
dc.contributor.authorLim, C.
dc.date.issued2011
dc.description.abstractGenetic evolutionary algorithms are effective and optimal test generation methods. However, the methods to select the algorithm parameters are often ad hoc, relying on empirical data. We used a Markov-based method to model the genetic evolutionary test generation process, parameterise the process characteristics, and derive analytical solutions for selecting the optimisation parameters. The method eliminates preliminary test generation calibration and experimentation effort needed to select these parameters, which are used in current practice.
dc.description.statementofresponsibilityAdriel Cheng and Cheng-Chew Lim
dc.identifier.citationJournal of Global Optimization, 2011; 51(4):743-751
dc.identifier.doi10.1007/s10898-011-9682-5
dc.identifier.issn0925-5001
dc.identifier.issn1573-2916
dc.identifier.orcidLim, C. [0000-0002-2463-9760]
dc.identifier.urihttp://hdl.handle.net/2440/69978
dc.language.isoen
dc.publisherKluwer Academic Publ
dc.relation.granthttp://purl.org/au-research/grants/arc/LP0454838
dc.rights© Springer Science+Business Media, LLC. 2011
dc.source.urihttps://doi.org/10.1007/s10898-011-9682-5
dc.subjectGenetic algorithm
dc.subjectParameter selection
dc.subjectMarkov model
dc.subjectHardware design verification
dc.titleMarkov modelling and parameterisation of genetic evolutionary test generations
dc.typeJournal article
pubs.publication-statusPublished

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