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dc.contributor.authorGladwin, Daniel*
dc.contributor.authorStewart, Paul*
dc.contributor.authorStewart, Jill*
dc.date.accessioned2016-08-25T11:26:40Z
dc.date.available2016-08-25T11:26:40Z
dc.date.issued2011-06
dc.identifier.citationA controlled migration genetic algorithm operator for hardware-in-the-loop experimentation 2011, 24 (4):586 Engineering Applications of Artificial Intelligenceen
dc.identifier.issn9521976
dc.identifier.doi10.1016/j.engappai.2011.01.006
dc.description.abstractIn this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num-ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al-gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre-sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.
dc.description.sponsorshipThis research was part-funded by the EPSRC research grant FPEC: Free Piston Energy Converteren
dc.language.isoenen
dc.publisherInternational Federation of Automatic Controlen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0952197611000169en
dc.rightsArchived with thanks to Engineering Applications of Artificial Intelligenceen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectGenetic Algorithmsen
dc.subjecthardware in the loopen
dc.titleA controlled migration genetic algorithm operator for hardware-in-the-loop experimentationen
dc.typeArticleen
dc.contributor.departmentUniversity of Sheffielden
dc.identifier.journalEngineering Applications of Artificial Intelligenceen
refterms.dateFOA2019-02-28T14:32:09Z
html.description.abstractIn this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num-ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al-gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre-sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the-loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.


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