LEADER 02696nam 2200469 450 001 9910484979003321 005 20210316110400.0 010 $a3-030-63773-5 024 7 $a10.1007/978-3-030-63773-6 035 $a(CKB)5460000000008533 035 $a(DE-He213)978-3-030-63773-6 035 $a(MiAaPQ)EBC6450969 035 $a(PPN)253250773 035 $a(EXLCZ)995460000000008533 100 $a20210316d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArchiving strategies for evolutionary multi-objective optimization algorithms /$fOliver Schu?tze, Carlos Herna?ndez 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIII, 234 p. 130 illus., 44 illus. in color.) 225 1 $aStudies in computational intelligence ;$vVolume 938 311 $a3-030-63772-7 327 $aIntroduction -- Multi-objective Optimization -- The Framework -- Computing the Entire Pareto Front -- Computing Gap Free Pareto Fronts -- Using Archivers within MOEAs -- Test Problems. 330 $aThis book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization. 410 0$aStudies in computational intelligence ;$vVolume 938. 606 $aComputer algorithms 615 0$aComputer algorithms. 676 $a005.1 700 $aSchu?tze$b Oliver$01222824 702 $aHernandez$b Carlos 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484979003321 996 $aArchiving strategies for evolutionary multi-objective optimization algorithms$92836734 997 $aUNINA