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03082nam a22004935i 4500 |
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978-3-642-40179-4 |
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DE-He213 |
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20151218141742.0 |
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cr nn 008mamaa |
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130807s2014 gw | s |||| 0|eng d |
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|a 9783642401794
|9 978-3-642-40179-4
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|a 10.1007/978-3-642-40179-4
|2 doi
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|a Q342
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|a COM004000
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|a Pintea, Camelia-Mihaela.
|e author.
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|a Advances in Bio-inspired Computing for Combinatorial Optimization Problems
|h [electronic resource] /
|c by Camelia-Mihaela Pintea.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2014.
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|a X, 188 p.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 57
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|a Part I Biological Computing and Optimization -- Part II Ant Algorithms -- Part III Bio-inspired Multi-Agent Systems -- Part IV Applications with Bio-inspired Algorithms -- Part V Conclusions and Remarks.
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|a "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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|a Engineering.
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|a Operations research.
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|a Decision making.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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|a Engineering.
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| 650 |
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|a Computational Intelligence.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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|a Operation Research/Decision Theory.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783642401787
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| 830 |
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|a Intelligent Systems Reference Library,
|x 1868-4394 ;
|v 57
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| 856 |
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|u http://dx.doi.org/10.1007/978-3-642-40179-4
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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