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008 150903s2014 gw | o |||| 0|eng d
020 _a9783642332067
_99783642332067
024 7 _a10.1007/9783642332067
_2doi
035 _avtls000360158
039 9 _a201509030602
_bVLOAD
_c201405070255
_dVLOAD
_y201402201426
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aQA75.5-76.95
100 1 _aBorenstein, Yossi.
_eeditor.
_9345747
245 1 0 _aTheory and Principled Methods for the Design of Metaheuristics /
_cedited by Yossi Borenstein, Alberto Moraglio.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2014.
300 _axx, 270 páginas 62 ilustraciones, 16 ilustraciones en color.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aNatural Computing Series,
_x1619-7127
500 _aSpringer eBooks
505 0 _aNo Free Lunch Theorems: Limitations and Perspectives of Metaheuristics -- Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms -- Rugged and Elementary Landscapes -- Single-Funnel and Multi-funnel Landscapes and Subthreshold Seeking Behavior -- Black-Box Complexity for Bounding the Performance of Randomized Search Heuristics -- Designing an Optimal Search Algorithm with Respect to Prior Information -- The Bayesian Search Game -- Principled Design of Continuous Stochastic Search: From Theory to Practice -- Parsimony Pressure Made Easy: Solving the Problem of Bloat in GP -- Experimental Analysis of Optimization Algorithms: Tuning and Beyond -- Formal Search Algorithms + Problem Characterizations = Executable Search Strategies.
520 _aMetaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex.   In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters.   With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aMoraglio, Alberto.
_eeditor.
_9336402
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9783642332050
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-33206-7
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c306458
_d306458