000 03448nam a22005295i 4500
001 978-3-642-17339-4
003 DE-He213
005 20140220082844.0
007 cr nn 008mamaa
008 130125s2013 gw | s |||| 0|eng d
020 _a9783642173394
_9978-3-642-17339-4
024 7 _a10.1007/978-3-642-17339-4
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aCOM031000
_2bisacsh
082 0 4 _a004.0151
_223
100 1 _aJansen, Thomas.
_eauthor.
245 1 0 _aAnalyzing Evolutionary Algorithms
_h[electronic resource] :
_bThe Computer Science Perspective /
_cby Thomas Jansen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aX, 255 p. 19 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x1619-7127
505 0 _aIntroduction -- Evolutionary Algorithms and Other Randomized Search Heuristics -- Theoretical Perspectives on Evolutionay Algorithms -- General Limits in Black-Box Optimization -- Methods for the Analysis of Evolutionary Algorithms -- Selected Topics in the Analysis of Evolutionary Algorithms -- App. A, Landau Notation -- App. B, Tail Estimations -- App. C, Martingales and Applications.
520 _aEvolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years.   In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods.   The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms.  
650 0 _aComputer science.
650 0 _aInformation theory.
650 0 _aArtificial intelligence.
650 0 _aMathematical optimization.
650 0 _aEngineering.
650 1 4 _aComputer Science.
650 2 4 _aTheory of Computation.
650 2 4 _aComputational Intelligence.
650 2 4 _aOptimization.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642173387
830 0 _aNatural Computing Series,
_x1619-7127
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-17339-4
912 _aZDB-2-SCS
999 _c96748
_d96748