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140604s2014 gw | s |||| 0|eng d |
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|a 9783319074948
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|a 10.1007/978-3-319-07494-8
|2 doi
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|a Q342
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|a UYQ
|2 bicssc
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|a COM004000
|2 bisacsh
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|a EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V
|h [electronic resource] /
|c edited by Alexandru-Adrian Tantar...[et al.].
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260 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2014.
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300 |
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|a xiv, 336 p. :
|b il.
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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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|a text file
|b PDF
|2 rda
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|a Advances in Intelligent Systems and Computing,
|x 2194-5357 ;
|v 288
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505 |
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|a Set Oriented Numerics -- Computational Game Theory -- Machine Learning Applied to Networks -- Complex Networks and Landscape Analysis -- Local Search and Optimization -- Genetic Programming -- Evolutionary Multiobjective Optimization -- Practical Aspects of Evolutionary Algorithms.
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520 |
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|a This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 1â_"4, 2014.The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitionerâ_Ts view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of Evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitionerâ_Ts perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the Computational Game Theory, Local Search and Optimization, Genetic Programming, Evolutionary Multi-objective optimization tracks.
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|a Engineering.
|9 259622
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|a Computational intelligence.
|9 259845
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2 |
4 |
|a Artificial Intelligence (incl. Robotics).
|9 259846
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700 |
1 |
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|a Tantar, Alexandru-Adrian,
|e ed.
|9 260474
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700 |
1 |
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|a Tantar, Emilia,
|e ed.
|9 260475
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700 |
1 |
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|a Sun, Jian-Qiao,
|e ed.
|9 261434
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700 |
1 |
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|a Zhang, Wei,
|e ed.
|9 261435
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700 |
1 |
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|a Ding, Qian,
|e ed.
|9 261436
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700 |
1 |
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|a Schutze, Oliver,
|e ed.
|9 261437
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700 |
1 |
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|a Emmerich, Michael,
|e ed.
|9 261438
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700 |
1 |
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|a Legrand, Pierrick,
|e ed.
|9 260478
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700 |
1 |
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|a Del Moral, Pierre,
|e ed.
|9 261439
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700 |
1 |
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|a Coello Coello, Carlos A,
|e ed.
|9 260473
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776 |
0 |
8 |
|i Printed edition:
|z 9783319074931
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856 |
4 |
0 |
|u http://dx.doi.org/10.1007/978-3-319-07494-8
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912 |
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|a ZDB-2-ENG
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929 |
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|a COM
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942 |
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|c EBK
|6 _
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950 |
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|a Engineering (Springer-11647)
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999 |
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|a SKV
|c 27998
|d 27998
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