Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems.

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dc.contributor.authorBrintrup, Alexandra Melike
dc.contributor.authorTakagi, Hideyuki
dc.contributor.authorTiwari, Ashutosh
dc.contributor.authorRamsden, Jeremy J.
dc.date.accessioned2008-05-07T12:56:28Z
dc.date.available2008-05-07T12:56:28Z
dc.date.issued2006-09
dc.description.abstractWe propose a sequential interactive genetic algorithm (IGA), multi-objective IGA and parallel IGA, and evaluate them with both simulated and real users. Combining human evaluation with an optimization system for engineering design enables us to embed domainspecific knowledge that is frequently hard to describe, i.e. subjective criteria, and design preferences. We introduce a new IGA technique to extend the previously introduced sequential single objective GA and multi-objective GA, viz. parallel IGA. Experimental evaluation of three algorithms with a multi-objective manufacturing plant layout design task shows that the multi-objective IGA and the parallel IGA clearly provide better results than the sequential IGA, and that the multi-objective IGA gives the most diverse results and fastest convergence to a stable set of qualitatively optimum solutions, although the parallel IGA provides the best quantitative fitness convergence.en_UK
dc.identifier.citationAlexandra Melike Brintrup, Hideyuki Takagi, Ashutosh Tiwari, Jeremy J. Ramsden; Evaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems. Journal of Biological Physics and Chemistry, Vol 6 No 3, 2006 pp137-146en_UK
dc.identifier.issn1512-0856
dc.identifier.urihttp://hdl.handle.net/1826/2528
dc.language.isoenen_UK
dc.publisherJointly by, Collegium Basilea (Institute of Advanced Study) and Association of Modern Scientific Investigation.en_UK
dc.relation.ispartofwww.amsi.ge/jbpc
dc.subjectinnovative designen_UK
dc.subjectsubjectivityen_UK
dc.subjectevolutionary computingen_UK
dc.titleEvaluation of sequential, multi-objective, and parallel interactive genetic algorithms for multi-objective optimization problems.en_UK
dc.typeArticleen_UK

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