Global optimization using memetic differential evolution with applications to low level machine vision

In recent years meta-heuristic optimization has gained popularity in industry as well as academia due to increased computational resources and advances in the algorithms employed. As it has become apparent that no single algorithm can be declared the best in all cases, a rise in hybrid, or memetic a...

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Main Author: Tirronen, Ville
Other Authors: University of Jyväskylä, Jyväskylän yliopisto
Format: Doctoral dissertation
Language:eng
Published: 2008
Online Access: https://jyx.jyu.fi/handle/123456789/75945
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author Tirronen, Ville
author2 University of Jyväskylä Jyväskylän yliopisto
author_facet Tirronen, Ville University of Jyväskylä Jyväskylän yliopisto Tirronen, Ville University of Jyväskylä Jyväskylän yliopisto
author_sort Tirronen, Ville
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description In recent years meta-heuristic optimization has gained popularity in industry as well as academia due to increased computational resources and advances in the algorithms employed. As it has become apparent that no single algorithm can be declared the best in all cases, a rise in hybrid, or memetic algorithms that can be designed on case by case basis can be observed. In this work memetic algorithms are studied in depth in the context of Differential Evolution Algorithm that is among the best modern generic optimization algorithms. A whole setting of Memetic Differential Evolution methods is discovered and empirically validated with the inclusion of fitness diversity based co-ordination and stochastic adaptation schemes. This work also studies the industrial problem of real-time paper web defect detection. This is a problem that is extremely constrained in time and as such permits no complex runtime solution. Thus, the problem has been formulated as an optimization problem utilizing a simple and efficient run-time model that is tuned to precision with rather more complex methods before applying it in the industrial field. The tools used for this are meta-heuristic optimization and especially, memetic optimization.
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As it has become apparent that no single algorithm can be declared the best in all cases, a rise in hybrid, or memetic algorithms that can be designed on case by case basis can be observed. In this work memetic algorithms are studied in depth in the context of Differential Evolution Algorithm that is among the best modern generic optimization algorithms. A whole setting of Memetic Differential Evolution methods is discovered and empirically validated with the inclusion of fitness diversity based co-ordination and stochastic adaptation schemes. This work also studies the industrial problem of real-time paper web defect detection. This is a problem that is extremely constrained in time and as such permits no complex runtime solution. Thus, the problem has been formulated as an optimization problem utilizing a simple and efficient run-time model that is tuned to precision with rather more complex methods before applying it in the industrial field. The tools used for this are meta-heuristic optimization and especially, memetic optimization.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Harri Hirvi (hirvi@jyu.fi) on 2021-05-25T13:21:24Z\r\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2021-05-25T13:21:24Z (GMT). No. of bitstreams: 0\r\n Previous issue date: 2008", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.relation.ispartofseries", "value": "Jyv\u00e4skyl\u00e4 studies in computing", "language": null, "element": "relation", "qualifier": "ispartofseries", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli I:</b> Tirronen, V., Neri, F., K\u00e4rkk\u00e4inen, T., Valjus, K., & Rossi, T. (2007). A Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. In <i>M. G. E. al (Ed.), Applications of Evolutionary Computing. Proceedings of EvoWorkshops 2007 (pp. 320-329). Springer. Lecture Notes in Computer Science, 4448. </i> DOI: <a href=\"https://doi.org/10.1007/978-3-540-71805-5_35\"target=\"_blank\"> 10.1007/978-3-540-71805-5_35</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli II:</b> Tirronen, V., Neri, F., K\u00e4rkk\u00e4inen, T., Valjus, K., & Rossi, T. (2008). An Enhanced Memetic Differential Evolution in Filter Design for Defect Detection in Paper Production. <i>Evolutionary Computation Journal, 16(4), 529-555.</i> DOI: <a href=\"https://doi.org/10.1162/evco.2008.16.4.529\"target=\"_blank\"> 10.1162/evco.2008.16.4.529</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli III:</b> Caponio, A., Neri, F., & Tirronen, V. (2008). Super-fit control adaptation in memetic differential evolution frameworks. <i>Soft Computing - A Fusion of Foundations, Methodologies and Applications, 13, 811.</i> DOI: <a href=\"https://doi.org/10.1007/s00500-008-0357-1\"target=\"_blank\"> 10.1007/s00500-008-0357-1</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli IV:</b> Neri, F., & Tirronen, V. (2009). Memetic Differential Evolution Frameworks in Filter Design for Defect Detection in Paper Production. In <i>Studies in Computational Intelligence (pp. 113-131). Springer.</i> DOI: <a href=\"https://doi.org/10.1007/978-3-642-01636-3_7\"target=\"_blank\"> 10.1007/978-3-642-01636-3_7</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli V:</b> Neri, F., Tirronen, V., K\u00e4rkk\u00e4inen, T., & Rossi, T. (2007). Fitness Diversity Based Adaptation in Multimeme Algorithms: A Comparative Study. In <i>Proceedings of the IEEE Congress on Evolutionary Computation,Special Session on Memetic Algorithms, Singapore (pp. 2374-2381).</i> DOI: <a href=\"https://doi.org/10.1109/cec.2007.4424768\"target=\"_blank\"> 10.1109/cec.2007.4424768</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli VI:</b> Tirronen, V., Neri, F., Valjus, K., & K\u00e4rkk\u00e4inen, T. (2008). On Memetic Differential Evolution Frameworks: a Study of Advantages and Limitations in Hybridization. In <i>Proceedings of the IEEE World Congress on Computational Intelligence (pp. 2135-2142).</i> DOI: <a href=\"https://doi.org/10.1109/cec.2008.4631082\"target=\"_blank\"> 10.1109/cec.2008.4631082</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli VII:</b> Tirronen, V., Neri, F., Valjus, K., & K\u00e4rkk\u00e4inen, T. (2008). The \"Natura Non Facit Saltus\" Principle in Memetic Computing. In <i>Proceedings of the IEEE World Congress on Computational Intelligence (pp. 3882-3889).</i> DOI: <a href=\"https://doi.org/10.1109/cec.2008.4631325\"target=\"_blank\"> 10.1109/cec.2008.4631325</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli VIII:</b> Tirronen, V., & Neri, F. (2009). Differential Evolution with Fitness Diversity Self-Adaptation. In <i>Nature-Inspired Algorithms for Optimisation (pp. 199-234). Springer. Studies in Computational Intelligence, 193.</i> DOI: <a href=\"https://doi.org/10.1007/978-3-642-00267-0_7\"target=\"_blank\"> 10.1007/978-3-642-00267-0_7</a>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.relation.haspart", "value": "<b>Artikkeli IX:</b> Tirronen, V., & Neri, F. (2007). A Fast Randomized Memetic Algorithm for Highly Multimodal Problems. In <i>Proceedings of EuroGEN 2007, Jyv\u00e4skyl\u00e4, Finland (pp. pg. 27).</i>", "language": null, "element": "relation", "qualifier": "haspart", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": null, "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.title", "value": "Global optimization using memetic differential evolution with applications to low level machine vision", "language": null, "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "doctoral thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:ISBN:978-951-39-8102-0", "language": null, "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_db06", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "doctoralThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.date.digitised", "value": "2021", "language": null, "element": "date", "qualifier": "digitised", "schema": "dc"}]
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spellingShingle Tirronen, Ville Global optimization using memetic differential evolution with applications to low level machine vision
title Global optimization using memetic differential evolution with applications to low level machine vision
title_full Global optimization using memetic differential evolution with applications to low level machine vision
title_fullStr Global optimization using memetic differential evolution with applications to low level machine vision Global optimization using memetic differential evolution with applications to low level machine vision
title_full_unstemmed Global optimization using memetic differential evolution with applications to low level machine vision Global optimization using memetic differential evolution with applications to low level machine vision
title_short Global optimization using memetic differential evolution with applications to low level machine vision
title_sort global optimization using memetic differential evolution with applications to low level machine vision
title_txtP Global optimization using memetic differential evolution with applications to low level machine vision
url https://jyx.jyu.fi/handle/123456789/75945 http://www.urn.fi/URN:ISBN:978-951-39-8102-0
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