000 03747nam a22005775i 4500
001 978-3-319-01128-8
003 DE-He213
005 20200421112052.0
007 cr nn 008mamaa
008 130613s2013 gw | s |||| 0|eng d
020 _a9783319011288
_9978-3-319-01128-8
024 7 _a10.1007/978-3-319-01128-8
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aEVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation IV
_h[electronic resource] :
_bInternational Conference held at Leiden University, July 10-13, 2013 /
_cedited by Michael Emmerich, Andre Deutz, Oliver Schuetze, Thomas B�ack, Emilia Tantar, Alexandru-Adrian Tantar, Pierre Del Moral, Pierrick Legrand, Pascal Bouvry, Carlos A. Coello.
264 1 _aHeidelberg :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXIV, 324 p. 140 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Intelligent Systems and Computing,
_x2194-5357 ;
_v227
505 0 _aMachine Learning and Probabilistic Models -- Complex Networks and Evolutionary Computation -- Diversity Oriented Optimization -- Set-oriented Numerics and Evolutionary Multiobjective Optimization -- Genetic Programming -- Robust Optimization.
520 _aNumerical and computational methods are nowadays used in a wide range of contexts in complex systems research, biology, physics, and engineering.  Over the last decades different methodological schools have emerged with emphasis on different aspects of computation, such as nature-inspired algorithms, set oriented numerics, probabilistic systems and Monte Carlo methods. Due to the use of different terminologies and emphasis on different aspects of algorithmic performance there is a strong need for a more integrated view and opportunities for cross-fertilization across particular disciplines. These proceedings feature 20 original publications from distinguished authors in the cross-section of computational sciences, such as machine learning algorithms and probabilistic models, complex networks and fitness landscape analysis, set oriented numerics and cell mapping, evolutionary multiobjective optimization, diversity-oriented search, and the foundations of genetic programming algorithms. By presenting cutting edge results with a strong focus on foundations and integration aspects this work presents a stepping stone towards efficient, reliable, and well-analyzed methods for complex systems management and analysis.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aEmmerich, Michael.
_eeditor.
700 1 _aDeutz, Andre.
_eeditor.
700 1 _aSchuetze, Oliver.
_eeditor.
700 1 _aB�ack, Thomas.
_eeditor.
700 1 _aTantar, Emilia.
_eeditor.
700 1 _aTantar, Alexandru-Adrian.
_eeditor.
700 1 _aMoral, Pierre Del.
_eeditor.
700 1 _aLegrand, Pierrick.
_eeditor.
700 1 _aBouvry, Pascal.
_eeditor.
700 1 _aCoello, Carlos A.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319011271
830 0 _aAdvances in Intelligent Systems and Computing,
_x2194-5357 ;
_v227
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-01128-8
912 _aZDB-2-ENG
942 _cEBK
999 _c57247
_d57247