LEADER 01009nam--2200361---450- 001 990005946570203316 005 20140507094840.0 010 $a0-521-62453-3 035 $a000594657 035 $aUSA01000594657 035 $a(ALEPH)000594657USA01 035 $a000594657 100 $a20140507d1998----km-y0itay50------ba 101 $aeng 102 $aGB 105 $a||||||||001yy 200 1 $a<> order of nature in Aristotle's Physics$eplace and the elements$fHelen S. Lang 210 $aCambridge$cCambridge University$d1998 215 $aXII,324 p.$d24 cm 410 0$12001 454 1$12001 461 1$1001-------$12001 606 0 $aAristotele . Fisica$xCommenti$2BNCF 676 $a509.38 700 1$aLANG,$bHelen S.$0160441 801 0$aIT$bsalbc$gISBD 912 $a990005946570203316 951 $aCF 69$b6814 DSA 959 $aBK 969 $aDSA 979 $aDSA$b90$c20140507$lUSA01$h0948 996 $aOrder of nature in Aristotle's Physics$9476334 997 $aUNISA LEADER 04544nam 22006495 450 001 9910437901503321 005 20250402063746.0 010 $a9783642306655 010 $a3642306659 024 7 $a10.1007/978-3-642-30665-5 035 $a(CKB)3390000000030192 035 $a(SSID)ssj0000746041 035 $a(PQKBManifestationID)11433391 035 $a(PQKBTitleCode)TC0000746041 035 $a(PQKBWorkID)10860551 035 $a(PQKB)11344751 035 $a(DE-He213)978-3-642-30665-5 035 $a(MiAaPQ)EBC3070843 035 $a(PPN)168317362 035 $a(EXLCZ)993390000000030192 100 $a20120811d2013 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMetaheuristics for Dynamic Optimization /$fedited by Enrique Alba, Amir Nakib, Patrick Siarry 205 $a1st ed. 2013. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013. 215 $a1 online resource (XXXII, 400 p.) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v433 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a9783642306648 311 08$a3642306640 320 $aIncludes bibliographical references and index. 327 $aFrom the Contents: Performance Analysis of Dynamic Optimization Algorithms -- Quantitative Performance Measures for Dynamic Optimization Problems -- Dynamic Function Optimization: The Moving Peaks Benchmark -- SRCS: a technique for comparing multiple algorithms under several factors in Dynamic Optimization Problems -- Dynamic Combinatorial Optimization Problems: A Fitness Landscape Analysis -- Two Approaches for Single and Multi-Objective Dynamic Optimization -- Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima -- Dynamic multi-objective optimization using PSO. 330 $aThis book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic  optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay. 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v433 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a519.6/4 701 $aAlba$b Enrique$0853126 701 $aNakib$b Amir$01760401 701 $aSiarry$b Patrick$0860327 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437901503321 996 $aMetaheuristics for dynamic optimization$94199360 997 $aUNINA