Advances in Bio-inspired Computing for Combinatorial Optimization Problems: 57 (Intelligent Systems Reference Library, 57) - Tapa dura
Sinopsis
"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.
Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.
Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.
This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
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"Advances in Bio-inspired Combinatorial Optimization Problems" illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.
Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.
Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.
This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems (eng)
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems (Intelligent Systems Reference Library, 57)
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Springer Berlin Heidelberg Aug 2013, 2013
ISBN 10: 3642401783
ISBN 13: 9783642401787
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -'Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems. 200 pp. Englisch. Nº de ref. del artículo: 9783642401787
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Springer Berlin Heidelberg, 2013
ISBN 10: 3642401783
ISBN 13: 9783642401787
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Librería: moluna, Greven, Alemania
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces new bio-inspired techniques based on ants, agents and virtual robots Solves real-life complex problems using the introduced bio-inspired techniques Recent research on Bio-inspired Computing for Combinatorial Optimization Problems. Nº de ref. del artículo: 5059550
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Condición: New. Print on Demand pp. x + 188 45 Illus. (3 Col.). Nº de ref. del artículo: 95271950
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Condición: New. PRINT ON DEMAND pp. x + 188. Nº de ref. del artículo: 1898173915
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems (Intelligent Systems Reference Library, 57, Band 57)
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Hardcover. Condición: Neu. Neu Neuware, auf Lager - 'Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems. Nº de ref. del artículo: INF1000764065
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Springer, Springer Aug 2013, 2013
ISBN 10: 3642401783
ISBN 13: 9783642401787
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents.This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 200 pp. Englisch. Nº de ref. del artículo: 9783642401787
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Advances in Bio-inspired Computing for Combinatorial Optimization Problems
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Hardcover. Condición: Brand New. 2014 edition. 200 pages. 9.25x6.50x0.75 inches. In Stock. Nº de ref. del artículo: x-3642401783
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