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dc.contributor.authorTirronen, Villeen
dc.contributor.authorNeri, Ferranteen
dc.contributor.authorK¨arkk¨ainen, Tommien
dc.contributor.authorMajava, Kirsien
dc.contributor.authorRossi, Tuomoen
dc.date.accessioned2012-04-11T09:41:36Z
dc.date.available2012-04-11T09:41:36Z
dc.date.issued2008
dc.identifier.citationTirronen, V., Neri, F., Kärkkäinen, T., Majava, K. and Rossi, T. (2008) An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. Evolutionary Computation Journal, 16 (4), pp. 529-555en
dc.identifier.issn1063-6560
dc.identifier.urihttp://hdl.handle.net/2086/5891
dc.description.abstractThis article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.en
dc.language.isoenen
dc.publisherMIT Pressen
dc.subjectmemetic algorithmsen
dc.subjectdifferential evolutionen
dc.subjectmultimeme algorithmsen
dc.subjectdigital filter designen
dc.subjectFIR filteren
dc.subjectpaper productionen
dc.subjectedge detectionen
dc.titleAn enhanced memetic differential evolution in filter design for defect detection in paper production.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1162/evco.2008.16.4.529
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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