000 02966nam a22004815i 4500
001 978-3-540-73192-4
003 DE-He213
005 20161121231204.0
007 cr nn 008mamaa
008 100301s2007 gw | s |||| 0|eng d
020 _a9783540731924
_9978-3-540-73192-4
024 7 _a10.1007/978-3-540-73192-4
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aSchaefer, Robert.
_eauthor.
245 1 0 _aFoundations of Global Genetic Optimization
_h[electronic resource] /
_cby Robert Schaefer.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2007.
300 _aXI, 222 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v74
505 0 _aGlobal optimization problems -- Basic models of genetic computations -- Asymptotic behavior of the artificial genetic systems -- Adaptation in genetic search -- Two-phase stochastic global optimization strategies -- Summary and perspectives of genetic algorithms in continuous global optimization.
520 _aThis book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540731917
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v74
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-73192-4
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c509869
_d509869