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Lumoojatar - Enchantress (Finnish Edition)
Published by Bod - Books on Demand, 2016
ISBN 10: 9523304755 ISBN 13: 9789523304758
Language: Finnish
Condition: New.
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Lumoojatar - Enchantress (Finnish Edition)
Published by Bod - Books on Demand, 2016
ISBN 10: 9523304755 ISBN 13: 9789523304758
Language: Finnish
Condition: New.
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Lumoojatar - Enchantress (Finnish Edition)
Published by Bod - Books on Demand, 2016
ISBN 10: 9523304755 ISBN 13: 9789523304758
Language: Finnish
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Lumoojatar - Enchantress:Intialaisia runoja Suomesta - Indian Poems on Finland
Published by BoD - Books on Demand 2016-04, 2016
ISBN 10: 9523304755 ISBN 13: 9789523304758
Language: Finnish
PF. Condition: New.
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Condition: As New. Unread book in perfect condition.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Data-Driven Evolutionary Modeling in Materials Technology
Published by Taylor & Francis Ltd, 2024
ISBN 10: 103206174X ISBN 13: 9781032061740
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. New copy - Usually dispatched within 4 working days. 453.
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Data-driven Evolutionary Modeling in Materials Technology
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Data-Driven Evolutionary Modeling in Materials Technology
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Condition: New. In.
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Data-driven Evolutionary Modeling in Materials Technology
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Data-Driven Evolutionary Modeling in Materials Technology (Paperback)
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 103206174X ISBN 13: 9781032061740
Language: English
Paperback. Condition: new. Paperback. Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc.Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms.This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials. This book presents the genetic and evolutionary, algorithms and strategies associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions including available professional and public domain codes and a gamut of recent applications. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Paperback. Condition: Brand New. 318 pages. 10.00x7.00x10.00 inches. In Stock.
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Simulated Evolution and Learning (Paperback)
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Language: English
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.
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Data-Driven Evolutionary Modeling in Materials Technology (Paperback)
Published by Taylor & Francis Ltd, London, 2024
ISBN 10: 103206174X ISBN 13: 9781032061740
Language: English
Paperback. Condition: new. Paperback. Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc.Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms.This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials. This book presents the genetic and evolutionary, algorithms and strategies associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions including available professional and public domain codes and a gamut of recent applications. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
-
Simulated Evolution and Learning: 8th International Conference, Seal 2010, Kanpur, India, December 1-4, 2010, Proceedings
Published by Springer-Verlag New York Inc, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Language: English
Paperback. Condition: Brand New. 2010 edition. 719 pages. 9.00x6.00x1.00 inches. In Stock.
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Simulated Evolution and Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Language: English
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.
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Condition: New.
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Condition: New. 1st edition NO-PA16APR2015-KAP.
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Simulated Evolution and Learning : 8th International Conference, SEAL 2010, Kanpur, India, December 1-4, 2010, Proceedings
Published by Springer, Berlin, Springer Berlin Heidelberg, Springer, 2010
ISBN 10: 3642172970 ISBN 13: 9783642172977
Language: English
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.
-
Condition: New.
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Condition: New.
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Data-Driven Evolutionary Modeling in Materials Technology
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Data-driven Evolutionary Modeling in Materials Technology
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: As New. Unread book in perfect condition.
