A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives

Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unify...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 20; H. 3; S. 358 - 369
Hauptverfasser:	Seada, Haitham, Deb, Kalyanmoy
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Design engineering Design optimization Equivalence Evolutionary Evolutionary algorithms Heuristic algorithms mono-objective optimization NSGA-III Objectives Optimization Optimization algorithms Representations Sociology Software Software algorithms Statistics Unified algorithms mono-objective optimization multiobjective optimization non-dominated sorting genetic algorithm (NSGA)-III Many-objective optimization unified algorithms
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unifying all three types of optimization problems together. Such a unified algorithm will allow users to work with a single software enabling one-time implementation of solution representation, operators, objectives, and constraints formulations across several objective dimensions. For the first time, we propose a unified evolutionary optimization algorithm for solving all three classes of problems specified above, based on the recently proposed elitist, guided nondominated sorting procedure, developed for solving many-objectives problems. Using a new niching-based selection procedure, our proposed unified algorithm automatically degenerates to an efficient equivalent population-based algorithm for each class. No extra parameters are needed. Extensive simulations are performed on unconstrained and constrained test problems having single-, two-, multi-, and many-objectives and on two engineering optimization design problems. Performance of the unified approach is compared to suitable population-based counterparts at each dimensional level. Results amply demonstrate the merit of our proposed unified approach and motivate similar studies for a richer understanding of the development of optimization algorithms.
AbstractList	Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unifying all three types of optimization problems together. Such a unified algorithm will allow users to work with a single software enabling one-time implementation of solution representation, operators, objectives, and constraints formulations across several objective dimensions. For the first time, we propose a unified evolutionary optimization algorithm for solving all three classes of problems specified above, based on the recently proposed elitist, guided nondominated sorting procedure, developed for solving many-objectives problems. Using a new niching-based selection procedure, our proposed unified algorithm automatically degenerates to an efficient equivalent population-based algorithm for each class. No extra parameters are needed. Extensive simulations are performed on unconstrained and constrained test problems having single-, two-, multi-, and many-objectives and on two engineering optimization design problems. Performance of the unified approach is compared to suitable population-based counterparts at each dimensional level. Results amply demonstrate the merit of our proposed unified approach and motivate similar studies for a richer understanding of the development of optimization algorithms.
Author	Seada, Haitham Deb, Kalyanmoy
Author_xml	– sequence: 1 givenname: Haitham surname: Seada fullname: Seada, Haitham email: seadahai@msu.edu organization: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA – sequence: 2 givenname: Kalyanmoy surname: Deb fullname: Deb, Kalyanmoy email: kdeb@msu.edu organization: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA
BookMark	eNp9kMFq3DAQQEVJoUnaDyi9GHrJId5qJNvyHMOyaQIJKTRpexNaa1S0eKWNZAear4-dDTnk0JNG8N4wvCN2EGIgxj4DXwBw_Ha7-rVcCA71QlQ1KmjfsUPACkrORXMwzbzFUqn2zwd2lPOGc6hqwEP2-6y4C955ssXqIfbj4GMw6V9xsxv81j-a-V_8SLEjOyYqXEzFTx_-9nRaXI_94HfzZIItrk2YrPWGusE_UP7I3jvTZ_r08h6zu_PV7fKivLr5frk8uyo7iWIoG2dRIhCXhpyluiGSBKbmLW-wsWsUhndCAaztWgo0JF1nAKUz1nGsrTxmJ_u9uxTvR8qD3vrcUd-bQHHMGlpRVwhtpSb06xt0E8cUpus0KBQKVaNmSu2pLsWcEznd-eE5w5CM7zVwPQfXc3A9B9cvwScT3pi75LdTzP86X_aOJ6JXXkFTiRblE5Rujjc
CODEN	ITEVF5
CitedBy_id	crossref_primary_10_1016_j_jmsy_2024_07_015 crossref_primary_10_1016_j_nucengdes_2023_112787 crossref_primary_10_1109_ACCESS_2020_2990567 crossref_primary_10_1016_j_entcs_2019_08_040 crossref_primary_10_1016_j_asoc_2017_01_029 crossref_primary_10_1109_TEVC_2017_2725902 crossref_primary_10_1016_j_apenergy_2025_126387 crossref_primary_10_1016_j_jcp_2018_10_015 crossref_primary_10_1061__ASCE_WR_1943_5452_0000774 crossref_primary_10_1109_TEVC_2018_2881955 crossref_primary_10_3390_w16213030 crossref_primary_10_1016_j_optlastec_2024_111755 crossref_primary_10_1515_nanoph_2022_0376 crossref_primary_10_3390_su13031424 crossref_primary_10_1016_j_engappai_2023_107381 crossref_primary_10_1109_TCYB_2022_3204030 crossref_primary_10_1038_s41598_024_54841_z crossref_primary_10_1093_bib_bby092 crossref_primary_10_1371_journal_pone_0321403 crossref_primary_10_3390_aerospace12030229 crossref_primary_10_1016_j_enbuild_2025_116100 crossref_primary_10_1016_j_jbiomech_2021_110575 crossref_primary_10_1109_TEVC_2021_3076514 crossref_primary_10_1038_s41598_024_62483_4 crossref_primary_10_1109_TII_2023_3316183 crossref_primary_10_1016_j_swevo_2019_05_009 crossref_primary_10_1109_TEVC_2016_2608507 crossref_primary_10_1016_j_scitotenv_2024_174842 crossref_primary_10_1109_TEVC_2016_2587749 crossref_primary_10_1016_j_energy_2024_134045 crossref_primary_10_1007_s12559_016_9395_7 crossref_primary_10_1016_j_asoc_2025_113762 crossref_primary_10_1631_FITEE_1900321 crossref_primary_10_1016_j_ejor_2020_03_040 crossref_primary_10_1111_mice_12917 crossref_primary_10_3390_e27050524 crossref_primary_10_1002_adts_202100576 crossref_primary_10_1016_j_agwat_2021_106755 crossref_primary_10_1016_j_jmapro_2024_10_061 crossref_primary_10_1061__ASCE_HE_1943_5584_0001896 crossref_primary_10_3390_electronics12244971 crossref_primary_10_3390_electronics10233034 crossref_primary_10_1016_j_swevo_2023_101444 crossref_primary_10_1109_TAP_2022_3177505 crossref_primary_10_1016_j_ress_2025_111275 crossref_primary_10_1016_j_jmsy_2022_11_014 crossref_primary_10_3390_electronics14010103 crossref_primary_10_1016_j_mtphys_2024_101438 crossref_primary_10_1016_j_nucengdes_2024_113611 crossref_primary_10_1016_j_swevo_2022_101185 crossref_primary_10_1016_j_swevo_2023_101262 crossref_primary_10_1109_TVT_2024_3433606 crossref_primary_10_3389_fenrg_2024_1362343 crossref_primary_10_1016_j_swevo_2019_02_003 crossref_primary_10_1080_08839514_2024_2398895 crossref_primary_10_3390_aerospace9030147 crossref_primary_10_3390_w13121670 crossref_primary_10_1016_j_cej_2024_152345 crossref_primary_10_1016_j_chroma_2023_464329 crossref_primary_10_1016_j_ins_2022_04_022 crossref_primary_10_1016_j_swevo_2019_100626 crossref_primary_10_1109_TEC_2022_3208129 crossref_primary_10_1016_j_envsoft_2021_105161 crossref_primary_10_1016_j_engappai_2025_111138 crossref_primary_10_1016_j_jcou_2024_102786 crossref_primary_10_1088_1748_3190_ab6dbb crossref_primary_10_1007_s13369_018_03712_7 crossref_primary_10_1016_j_swevo_2024_101770 crossref_primary_10_1109_TEVC_2018_2844286 crossref_primary_10_1029_2021WR031104 crossref_primary_10_1007_s10664_023_10431_7 crossref_primary_10_1016_j_knosys_2021_107392 crossref_primary_10_1002_sys_21811 crossref_primary_10_1371_journal_pone_0284110 crossref_primary_10_1016_j_jtice_2023_105287 crossref_primary_10_1109_ACCESS_2018_2890461 crossref_primary_10_1109_TEVC_2018_2871362 crossref_primary_10_1016_j_ins_2018_07_012 crossref_primary_10_1016_j_asoc_2023_110472 crossref_primary_10_1016_j_jhydrol_2018_07_056 crossref_primary_10_1109_ACCESS_2021_3070981 crossref_primary_10_1109_ACCESS_2018_2875122 crossref_primary_10_1109_TCYB_2018_2825343 crossref_primary_10_1109_TETCI_2024_3360331 crossref_primary_10_1162_evco_a_00226 crossref_primary_10_1016_j_eswa_2024_125082 crossref_primary_10_2514_1_C037778 crossref_primary_10_1002_cpe_70007 crossref_primary_10_1016_j_asoc_2022_109940 crossref_primary_10_1109_TETCI_2023_3279666 crossref_primary_10_1109_TIM_2018_2809839 crossref_primary_10_3390_nano12224097 crossref_primary_10_1016_j_agwat_2021_107069
Cites_doi	10.1162/EVCO_a_00075 10.1162/106365600568202 10.1162/evco.1994.2.3.221 10.1007/3-540-36970-8_27 10.1137/S1052623496307510 10.2307/30044198 10.1007/978-3-642-01020-0_33 10.1109/CEC.2008.4631121 10.1109/CEC.2010.5586247 10.1007/3-540-45356-3_83 10.1007/3-540-44719-9_9 10.1023/A:1015059928466 10.1007/3-540-44719-9_24 10.1109/TEVC.2010.2058117 10.1007/978-3-662-03423-1_27 10.1016/j.ejor.2006.06.042 10.1109/TSMCB.2008.926329 10.1007/978-3-642-05258-3_56 10.1109/SBRN.2012.20 10.1016/j.advengsoft.2011.05.014 10.1109/TEVC.2012.2204264 10.1109/TEVC.2007.892759 10.1007/3-540-45356-3_82 10.1109/TEVC.2012.2204403 10.1081/AMP-120022019 10.1007/978-1-4757-5184-0 10.1109/TEVC.2010.2093579 10.1109/TEVC.2007.910138 10.1016/j.ces.2004.06.012 10.1109/TEVC.2013.2281535 10.2307/2315353 10.1007/978-3-642-10439-8_45 10.1109/TEVC.2010.2077298 10.1109/CEC.2008.4631063 10.1109/TEVC.2013.2281534 10.1109/TCAPT.2004.831775
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016
DBID	97E RIA RIE AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
DOI	10.1109/TEVC.2015.2459718
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ANTE: Abstracts in New Technology & Engineering Engineering Research Database
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional Engineering Research Database ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	Technology Research Database Technology Research Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Statistics Computer Science
EISSN	1941-0026
EndPage	369
ExternalDocumentID	4073927861 10_1109_TEVC_2015_2459718 7164289
Genre	orig-research
GrantInformation_xml	– fundername: Binational Fulbright Commission in Egypt – fundername: AMIDEAST Washington, DC, through his Ph.D. Fulbright Grant
GroupedDBID	-~X .DC 0R~ 29I 4.4 5GY 5VS 6IF 6IK 6IL 6IN 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ADZIZ AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 EBS EJD HZ~ H~9 IEGSK IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RIL RNS TN5 VH1 AAYXX CITATION 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
ID	FETCH-LOGICAL-c392t-6fd9391e03aefde56ee3e1a5080696db92a0c2711bdb329ae3fca193fadf095d3
IEDL.DBID	RIE
ISICitedReferencesCount	125
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000377620600003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1089-778X
IngestDate	Thu Oct 02 10:29:20 EDT 2025 Sun Nov 09 06:15:08 EST 2025 Sat Nov 29 03:13:47 EST 2025 Tue Nov 18 22:18:17 EST 2025 Tue Aug 26 16:42:57 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	mono-objective optimization multiobjective optimization non-dominated sorting genetic algorithm (NSGA)-III Many-objective optimization unified algorithms
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c392t-6fd9391e03aefde56ee3e1a5080696db92a0c2711bdb329ae3fca193fadf095d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PQID	1792797677
PQPubID	85418
PageCount	12
ParticipantIDs	ieee_primary_7164289 proquest_journals_1792797677 crossref_citationtrail_10_1109_TEVC_2015_2459718 proquest_miscellaneous_1825491847 crossref_primary_10_1109_TEVC_2015_2459718
PublicationCentury	2000
PublicationDate	2016-June 2016-6-00 20160601
PublicationDateYYYYMMDD	2016-06-01
PublicationDate_xml	– month: 06 year: 2016 text: 2016-June
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on evolutionary computation
PublicationTitleAbbrev	TEVC
PublicationYear	2016
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref35 ref13 zou (ref19) 2008; 38 ref12 ref37 ref15 ref36 ref14 ref31 ref30 ref33 ref11 ref32 ref10 ref2 ref17 ref38 ref16 ref18 deb (ref39) 1994; 9 goh (ref3) 2009; 186 deb (ref1) 2001; 16 ref24 ref45 ref23 ref26 ref25 ref20 ref42 ref22 ref44 ref21 ref43 ref28 saxena (ref34) 2007 ref27 ref8 knowles (ref41) 2007 tan (ref4) 2006 bader (ref29) 2008 ref9 zitzler (ref7) 2001 ref6 ref5 liang (ref40) 2006; 41
References_xml	– ident: ref15 doi: 10.1162/EVCO_a_00075 – ident: ref42 doi: 10.1162/106365600568202 – ident: ref5 doi: 10.1162/evco.1994.2.3.221 – ident: ref13 doi: 10.1007/3-540-36970-8_27 – ident: ref35 doi: 10.1137/S1052623496307510 – ident: ref45 doi: 10.2307/30044198 – year: 2001 ident: ref7 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm – volume: 16 year: 2001 ident: ref1 publication-title: Multi-Objective Optimization Using Evolutionary Algorithms – ident: ref17 doi: 10.1007/978-3-642-01020-0_33 – ident: ref14 doi: 10.1109/CEC.2008.4631121 – ident: ref18 doi: 10.1109/CEC.2010.5586247 – ident: ref6 doi: 10.1007/3-540-45356-3_83 – ident: ref9 doi: 10.1007/3-540-44719-9_9 – volume: 9 start-page: 1 year: 1994 ident: ref39 article-title: Simulated binary crossover for continuous search space publication-title: Complex Syst – ident: ref37 doi: 10.1023/A:1015059928466 – start-page: 919 year: 2007 ident: ref34 article-title: Trading on infeasibility by exploiting constraints criticality through multi-objectivization: A system design perspective publication-title: Proc IEEE Congr Evol Comput (CEC) – ident: ref10 doi: 10.1007/3-540-44719-9_24 – volume: 41 start-page: 8 year: 2006 ident: ref40 article-title: Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization publication-title: J Appl Mech – ident: ref12 doi: 10.1109/TEVC.2010.2058117 – ident: ref36 doi: 10.1007/978-3-662-03423-1_27 – ident: ref28 doi: 10.1016/j.ejor.2006.06.042 – volume: 38 start-page: 1402 year: 2008 ident: ref19 article-title: A new evolutionary algorithm for solving many-objective optimization problems publication-title: IEEE Trans Syst Man Cybern B Cybern doi: 10.1109/TSMCB.2008.926329 – ident: ref11 doi: 10.1007/978-3-642-05258-3_56 – ident: ref24 doi: 10.1109/SBRN.2012.20 – ident: ref38 doi: 10.1016/j.advengsoft.2011.05.014 – ident: ref30 doi: 10.1109/TEVC.2012.2204264 – ident: ref22 doi: 10.1109/TEVC.2007.892759 – ident: ref8 doi: 10.1007/3-540-45356-3_82 – ident: ref23 doi: 10.1109/TEVC.2012.2204403 – ident: ref31 doi: 10.1081/AMP-120022019 – ident: ref2 doi: 10.1007/978-1-4757-5184-0 – ident: ref16 doi: 10.1109/TEVC.2010.2093579 – ident: ref21 doi: 10.1109/TEVC.2007.910138 – ident: ref33 doi: 10.1016/j.ces.2004.06.012 – ident: ref25 doi: 10.1109/TEVC.2013.2281535 – year: 2007 ident: ref41 publication-title: Multiobjective Problem Solving from Nature From Concepts to Applications – year: 2006 ident: ref4 publication-title: Multiobjective Evolutionary Algorithms and Applications – ident: ref44 doi: 10.2307/2315353 – ident: ref20 doi: 10.1007/978-3-642-10439-8_45 – ident: ref43 doi: 10.1109/TEVC.2010.2077298 – ident: ref27 doi: 10.1109/CEC.2008.4631063 – year: 2008 ident: ref29 article-title: HypE: An algorithm for fast hypervolumebased many-objective optimization – ident: ref26 doi: 10.1109/TEVC.2013.2281534 – ident: ref32 doi: 10.1109/TCAPT.2004.831775 – volume: 186 year: 2009 ident: ref3 article-title: Evolutionary multi-objective optimization in uncertain environments publication-title: Issues and Algorithms
SSID	ssj0014519
Score	2.5248797
Snippet	Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	358
SubjectTerms	Algorithm design and analysis Algorithms Design engineering Design optimization Equivalence Evolutionary Evolutionary algorithms Heuristic algorithms mono-objective optimization NSGA-III Objectives Optimization Optimization algorithms Representations Sociology Software Software algorithms Statistics Unified algorithms
Title	A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives
URI	https://ieeexplore.ieee.org/document/7164289 https://www.proquest.com/docview/1792797677 https://www.proquest.com/docview/1825491847
Volume	20
WOSCitedRecordID	wos000377620600003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0026 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014519 issn: 1089-778X databaseCode: RIE dateStart: 19970101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB508aAHH6vi-iKCJ9lqH9tkcxRZ8eIDfO2tJM3kIGtXVnfBf-8kzRZBEbwVmklLv2TyTecFcBxrWfZQ24gUMEZ0HotIKiMioxTntF56QmrfbELc3PSHQ3m3AN0mF4ZEfPAZnrpL78s343LqfpWdOW5PBsIiLAoh6lytxmPgyqTUwfSSGGN_GDyYSSzPHgZPFy6IKz9Ne8SfXX-Pb2eQb6ryQxP74-Vy7X8vtg6rgUay8xr3DVjAqg1r8xYNLOzYNqx8qzfYhmVHLevKzJvwfM6IcFqioGwwCwtQTT7ZLSmR15CdyXwegZlOkBG5Zfc0ywi77DpEIXaZqgy7Jn3CbvVLrTrft-DxcvBwcRWFNgtRSeToI-LWyEwmGGcKrcGcI2aYKGJuMZfcaJmquExFkmijs1QqzGypiPdZZSwRNJNtQ6saV7gDLCMdgUbmed_yXqK4znMtjMlI2NjcqA7E8w9flKEGuWuFMSq8LRLLwmFVOKyKgFUHThqRt7oAx1-DNx04zcCASwf25-gWYYu-F6SJUkFkTIgOHDW3aXM5j4mqcDylMd5-JiNY7P4-8x4s0_N5HTu2D62PyRQPYKmcEZiTQ79CvwDApeST
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dSxwxEB_UFrQPak-l50dNwadyq_uZXB5FTiz1zoKnvbcl2Uweit2T0xP633eSzS2CIvi2sEkI-5tMfrPzBXAUa1nlqG1EChgjuo9FJJURkVGKc5KXXEjtm02I0ag_mchfS9Brc2Foig8-w2P36H35ZlrN3a-yE8ftyUBYhg9FnqdJk63V-gxcoZQmnF4SZ-xPgg8zieXJeHB75sK4iuM0JwbtOnw8u4V8W5UXuthfMOcb79vaJqwHIslOG-Q_wxLWHdhYNGlg4cx24NOzioMdWHPksqnNvAW_TxlRTksklA2eggiq2T92RWrkb8jPZD6TwMxnyIjesmta5Q57bBjiEHtM1YYNSaOwK_2nUZ4P23BzPhifXUSh0UJUET16jLg1MpMJxplCa7DgiBkmirhbzCU3WqYqrlKRJNroLJUKM1spYn5WGUsUzWQ7sFJPa_wCLCMtgUYWRd_yPFFcF4UWxmQ02djCqC7Eiw9fVqEKuWuGcVd6aySWpcOqdFiVAasufG-n3DclON4avOXAaQcGXLqwv0C3DIf0oSRdlAqiY0J04Vv7mo6X85moGqdzGuMtaDKDxe7rKx_C6sV4eFle_hj93IM12gtvIsn2YeVxNscD-Fg9EbCzr15a_wPIfefa
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Unified+Evolutionary+Optimization+Procedure+for+Single%2C+Multiple%2C+and+Many+Objectives&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Seada%2C+Haitham&rft.au=Deb%2C+Kalyanmoy&rft.date=2016-06-01&rft.issn=1089-778X&rft.eissn=1941-0026&rft.volume=20&rft.issue=3&rft.spage=358&rft.epage=369&rft_id=info:doi/10.1109%2FTEVC.2015.2459718&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-778X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-778X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-778X&client=summon