03140nam a22004215i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137040000900172082001400181100003500195245010800230264003800338300002100376336002600397337002600423338003900449347002400488490006400512505019300576520147900769650001702248650003102265650001702296650001802313650006202331650003302393700003502426710003402461773002002495776003602515830006402551856010302615978-0-387-74740-8DE-He21320260521091950.0cr nn 008mamaa100301s2008    xxu|    s    |||| 0|eng d  a9780387747408  a997803877474087 a10.1007/978-0-387-74740-82doi  cCICY04a519.62231 aZhigljavsky, Anatoly.eauthor.10aStochastic Global Optimizationh[recurso electrónico] /cby Anatoly Zhigljavsky, Antanas Žilinskas. 1aBoston, MA :bSpringer US,c2008.  bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aSpringer Optimization and Its Applications,x1931-6828 ;v90 aBasic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.  aThis book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory. Key features: * Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods; * Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms; * Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms; *Provides a thorough description of the methods based on statistical models of objective function; *Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization. Stochastic Global Optimization is intended for mature researchers and graduate students interested in global optimization, operations research, computer science, probability, statistics, computational and applied mathematics, mechanical and chemical engineering, and many other fields where methods of global optimization can be used. 0aMATHEMATICS. 0aMATHEMATICAL OPTIMIZATION.14aMATHEMATICS.24aOPTIMIZATION.24aCALCULUS OF VARIATIONS AND OPTIMAL CONTROL; OPTIMIZATION.24aAPPLICATIONS OF MATHEMATICS.1 aŽilinskas, Antanas.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387740225 0aSpringer Optimization and Its Applications,x1931-6828 ;v940uhttp://dx.doi.org/10.1007/978-0-387-74740-8zVer el texto completo en las instalaciones del CICY