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101109s2010 gw | s |||| 0|eng d |
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|a 9783642165443
|9 978-3-642-16544-3
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|a 10.1007/978-3-642-16544-3
|2 doi
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|a QA76.9.A43
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|a UMB
|2 bicssc
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|a COM051300
|2 bisacsh
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|a 005.1
|2 23
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| 100 |
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|a Neumann, Frank.
|e author.
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| 245 |
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|a Bioinspired Computation in Combinatorial Optimization
|b Algorithms and Their Computational Complexity /
|c by Frank Neumann, Carsten Witt.
|h [electronic resource] :
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| 264 |
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1 |
|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg,
|c 2010.
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| 300 |
# |
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|a XII, 216 p.
|b online resource.
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| 336 |
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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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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Natural Computing Series,
|x 1619-7127
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| 505 |
0 |
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|a Introduction -- Combinatorial Optimization and Computational Complexity -- Stochastic Search Algorithms -- Analyzing Stochastic Search Algorithms -- Minimum Spanning Trees -- Maximum Matchings -- Makespan Scheduling -- Shortest Paths -- Eulerian Cycles -- Multi-objective Minimum Spanning Trees -- Minimum Spanning Trees Made Easier -- Covering Problems -- Cutting Problems -- App. A.1, Probability Distributions -- App. A.2, Deviation Inequalities -- App. A.3, Other Useful Formulas -- References.
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| 520 |
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|a Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these search heuristics. This is the first book to explain the most important results achieved in this area. The authors show how runtime behavior can be analyzed in a rigorous way. in particular for combinatorial optimization. They present well-known problems such as minimum spanning trees, shortest paths, maximum matching, and covering and scheduling problems. Classical single-objective optimization is examined first. They then investigate the computational complexity of bioinspired computation applied to multiobjective variants of the considered combinatorial optimization problems, and in particular they show how multiobjective optimization can help to speed up bioinspired computation for single-objective optimization problems. This book will be valuable for graduate and advanced undergraduate courses on bioinspired computation, as it offers clear assessments of the benefits and drawbacks of various methods. It offers a self-contained presentation, theoretical foundations of the techniques, a unified framework for analysis, and explanations of common proof techniques, so it can also be used as a reference for researchers in the areas of natural computing, optimization and computational complexity. �
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| 650 |
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|a Computer science.
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| 650 |
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|a Information theory.
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| 650 |
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|a Computer software.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Mathematical optimization.
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| 650 |
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4 |
|a Computer Science.
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| 650 |
2 |
4 |
|a Algorithm Analysis and Problem Complexity.
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| 650 |
2 |
4 |
|a Optimization.
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| 650 |
2 |
4 |
|a Theory of Computation.
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| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
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| 700 |
1 |
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|a Witt, Carsten.
|e author.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783642165436
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| 830 |
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|a Natural Computing Series,
|x 1619-7127
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-16544-3
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| 912 |
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|a ZDB-2-SCS
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| 950 |
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|a Computer Science (Springer-11645)
|