LEADER 04047nam 22007935 450 001 9910972939503321 005 20250609110055.0 010 $a1-281-13893-2 010 $a9786611138936 010 $a0-387-74740-0 024 7 $a10.1007/978-0-387-74740-8 035 $a(CKB)1000000000413587 035 $a(EBL)337204 035 $a(OCoLC)261324691 035 $a(SSID)ssj0000251545 035 $a(PQKBManifestationID)11200727 035 $a(PQKBTitleCode)TC0000251545 035 $a(PQKBWorkID)10170585 035 $a(PQKB)10159560 035 $a(DE-He213)978-0-387-74740-8 035 $a(Au-PeEL)EBL337204 035 $a(CaPaEBR)ebr10218002 035 $a(CaONFJC)MIL113893 035 $a(PPN)123733952 035 $a(MiAaPQ)EBC337204 035 $a(EXLCZ)991000000000413587 100 $a20100301d2008 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStochastic Global Optimization /$fby Anatoly Zhigljavsky, Antanasz Zilinskas 205 $a1st ed. 2008. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2008. 215 $a1 online resource (270 p.) 225 1 $aSpringer Optimization and Its Applications,$x1931-6836 ;$v9 300 $aDescription based upon print version of record. 311 08$a1-4419-4485-0 311 08$a0-387-74022-8 320 $aIncludes bibliographical references and index. 327 $aBasic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions. 330 $aThis book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory. Key features: * Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods; * Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms; * Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms; *Provides a thorough description of the methods based on statistical models of objective function; *Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization. Stochastic Global Optimization is intended for mature researchers and graduate students interested in global optimization, operations research, computer science, probability, statistics, computational and applied mathematics, mechanical and chemical engineering, and many other fields where methods of global optimization can be used. 410 0$aSpringer Optimization and Its Applications,$x1931-6836 ;$v9 606 $aMathematical optimization 606 $aProbabilities 606 $aStatistics 606 $aOptimization 606 $aProbability Theory 606 $aStatistical Theory and Methods 615 0$aMathematical optimization. 615 0$aProbabilities. 615 0$aStatistics. 615 14$aOptimization. 615 24$aProbability Theory. 615 24$aStatistical Theory and Methods. 676 $a519.62 686 $a510$2sdnb 686 $aSK 870$2rvk 686 $aSK 880$2rvk 700 $aZhigli?avskii?$b A. A$g(Anatolii? Aleksandrovich)$0468354 701 $aZhilinskas$b A$01817746 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910972939503321 996 $aStochastic Global Optimization$94375866 997 $aUNINA