Language: English
Published by Aavishkar Publishers & Distributors
ISBN 10: 8179103714 ISBN 13: 9788179103715
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. pp. xiv + 210, Illus., Maps.
Language: English
Published by Aavishkar Publishers & Distributors
ISBN 10: 8179103714 ISBN 13: 9788179103715
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. pp. xiv + 210.
Language: English
Published by LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659917494 ISBN 13: 9783659917493
Seller: moluna, Greven, Germany
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Language: English
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: new. Paperback. ThisLNCSvolumecontainsthepaperspresentedatthe8thSimulatedEvolution and Learning (SEAL 2010) Conference held during December 1-4, 2010 at the Indian Institute of Technology Kanpur in India. SEAL is a prestigious inter- tional conference series in evolutionaryoptimization and machine learning. This biennial event started in Seoul, South Korea in 1996 and was thereafter held in Canberra, Australia in 1998, Nagoya, Japan in 2000, Singapore in 2002, Busan, South Korea in 2004,Hefei, China in 2006and Melbourne, Australia in 2008. SEAL 2010 received 141 paper submissions in total from 30 countries. After a rigorous peer-review process involving 431 reviews in total (averaging a little morethan3reviewsperpaper),60full-lengthand19shortpaperswereaccepted for presentation (both oral and poster) at the conference. The full-length papers alonecorrespondtoa42. 6%acceptancerateandshortpapersaddanother13. 5%. ThepapersincludedinthisLNCSvolumecoverawiderangeoftopicsinsi- latedevolutionandlearning. Theacceptedpapershavebeenclassi?edintothef- lowingmaincategories:(a)theoreticaldevelopments,(b)evolutionaryalgorithms andapplications,(c)learningmethodologies,(d)multi-objectiveevolutionary- gorithms and applications,(e) hybrid algorithms and (f) industrial applications.The conference featured three distinguished keynote speakers. Narendra Karmarkar's talk on "Beyond Convexity: New Perspectives in Computational Optimization" focused on providing new theoretical concepts for non-convex optimization and indicated a rich connection between optimization and ma- ematical physics and also showed a deep signi?cance of advanced geometry to optimization. The advancement of optimization theory for non-convex problems is bene?cial for meta-heuristic optimization algorithms such as evolutionary - gorithms. Manindra Agrawal's talk on "PRIMES is in P" provided a mu- improved version of his celebrated and ground-breaking 2002 work on poly- mial time algorithm for testing prime numbers. The theoretical computation work presented in this keynote lecture should be motivating for the evolutionary optimization and machine learning community at large. Constitutes the proceedings of the 8th International Conference on Simulated Evolution and Learning, SEAL 2010, held in Kanpur, India, in December 2010. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
Language: English
Published by Springer-Verlag New York Inc, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 2010 edition. 719 pages. 9.00x6.00x1.00 inches. In Stock.
Language: English
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Seller: moluna, Greven, Germany
Condition: New. State-of-the-art researchFast-track conference proceedingsUnique visibilityInvited Paper.- Beyond Convexity: New Perspectives in Computational Optimization.- Theoretical Developments.- Optimal ?-Distributions for the Hypervolume.
Language: English
Published by Springer, Berlin, Springer, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Neuware - ThisLNCSvolumecontainsthepaperspresentedatthe8thSimulatedEvolution and Learning (SEAL 2010) Conference held during December 1 4, 2010 at the Indian Institute of Technology Kanpur in India. SEAL is a prestigious inter- tional conference series in evolutionaryoptimization and machine learning. This biennial event started in Seoul, South Korea in 1996 and was thereafter held in Canberra, Australia in 1998, Nagoya, Japan in 2000, Singapore in 2002, Busan, South Korea in 2004,Hefei, China in 2006and Melbourne, Australia in 2008. SEAL 2010 received 141 paper submissions in total from 30 countries. After a rigorous peer-review process involving 431 reviews in total (averaging a little morethan3reviewsperpaper),60full-lengthand19shortpaperswereaccepted for presentation (both oral and poster) at the conference. The full-length papers alonecorrespondtoa42. 6%acceptancerateandshortpapersaddanother13. 5%. ThepapersincludedinthisLNCSvolumecoverawiderangeoftopicsinsi- latedevolutionandlearning. Theacceptedpapershavebeenclassi edintothef- lowingmaincategories:(a)theoreticaldevelopments,(b)evolutionaryalgorithms andapplications,(c)learningmethodologies,(d)multi-objectiveevolutionary- gorithms and applications,(e) hybrid algorithms and (f) industrial applications. The conference featured three distinguished keynote speakers. Narendra Karmarkar s talk on Beyond Convexity: New Perspectives in Computational Optimization focused on providing new theoretical concepts for non-convex optimization and indicated a rich connection between optimization and ma- ematical physics and also showed a deep signi cance of advanced geometry to optimization. The advancement of optimization theory for non-convex problems is bene cial for meta-heuristic optimization algorithms such as evolutionary - gorithms. Manindra Agrawal s talk on PRIMES is in P provided a mu- improved version of his celebrated and ground-breaking 2002 work on poly- mial time algorithm for testing prime numbers. The theoretical computation work presented in this keynote lecture should be motivating for the evolutionary optimization and machine learning community at large.