LEADER 02071nam  2200409   450 
001 9910830755503321
005 20200610221238.8
010   $a1-119-61240-3
010   $a1-119-61247-0
010   $a1-119-61236-5
035   $a(CKB)4100000007934807
035   $a(MiAaPQ)EBC5748879
035   $a(CaSebORM)9781786304094
035   $a(EXLCZ)994100000007934807
100   $a20190427d2019      uy             0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIterative optimizers $edifficulty measures and benchmarks /$fMaurice Clerc
205   $a1st edition
210  1$aHoboken, New Jersey :$cISTE :$cWiley,$d2019.
215   $a1 online resource (215 pages)
311   $a1-78630-409-0 
330   $aAlmost every month, a new optimization algorithm is proposed, often accompanied by the claim that it is superior to all those that came before it. However, this claim is generally based on the algorithm's performance on a specific set of test cases, which are not necessarily representative of the types of problems the algorithm will face in real life. This book presents the theoretical analysis and practical methods (along with source codes) necessary to estimate the difficulty of problems in a test set, as well as to build bespoke test sets consisting of problems with varied difficulties. The book formally establishes a typology of optimization problems, from which a reliable test set can be deduced. At the same time, it highlights how classic test sets are skewed in favor of different classes of problems, and how, as a result, optimizers that have performed well on test problems may perform poorly in real life scenarios.
606   $aMathematical optimization
615  0$aMathematical optimization.
676   $a519.3 
700   $aClerc$b Maurice$0845965
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910830755503321
996   $aIterative optimizers$94065263
997   $aUNINA