A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections

Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the searc...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 19; no. 4; pp. 592 - 605
Main Authors:	Jixiang Cheng, Yen, Gary G., Gexiang Zhang
Format:	Journal Article
Language:	English
Published:	IEEE 01.08.2015
Subjects:	Algorithm design and analysis Convergence directional diversity Evolutionary computation favorable convergence Maintenance engineering many- objective evolutionary algorithm Many-objective optimization problem Optimization Sociology Statistics many-objective optimization problem (MaOP) many-objective evolutionary algorithm (MaOEA) Directional diversity (DD) favorable convergence (FC)
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the search toward the Pareto front and the ineffective design in diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity (DD) and favorable convergence (FC). The main features are the enhancement of two selection schemes to facilitate both convergence and diversity. In the algorithm, a mating selection based on FC is applied to strengthen selection pressure while an environmental selection based on DD and FC is designed to balance diversity and convergence. The proposed algorithm is tested on 64 instances of 16 MaOPs with diverse characteristics and compared with seven state-of-the-art algorithms. Experimental results show that the proposed MaOEA performs competitively with respect to chosen state-of-the-art designs.
AbstractList	Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their performances deteriorate severely when handling many-objective optimization problems (MaOPs) due to the loss of selection pressure to drive the search toward the Pareto front and the ineffective design in diversity maintenance mechanism. This paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity (DD) and favorable convergence (FC). The main features are the enhancement of two selection schemes to facilitate both convergence and diversity. In the algorithm, a mating selection based on FC is applied to strengthen selection pressure while an environmental selection based on DD and FC is designed to balance diversity and convergence. The proposed algorithm is tested on 64 instances of 16 MaOPs with diverse characteristics and compared with seven state-of-the-art algorithms. Experimental results show that the proposed MaOEA performs competitively with respect to chosen state-of-the-art designs.
Author	Yen, Gary G. Gexiang Zhang Jixiang Cheng
Author_xml	– sequence: 1 surname: Jixiang Cheng fullname: Jixiang Cheng email: chengjixiang0106@126.com organization: Sch. of Electr. Eng., Southwest Jiaotong Univ., Chengdu, China – sequence: 2 givenname: Gary G. surname: Yen fullname: Yen, Gary G. email: gyen@okstate.edu organization: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA – sequence: 3 surname: Gexiang Zhang fullname: Gexiang Zhang email: zhgxdylan@126.com organization: Sch. of Electr. Eng., Southwest Jiaotong Univ., Chengdu, China
BookMark	eNp9kE1LAzEQhoNUsK3-APGSP7A1n83mWMpqhUoP1q_Tkk2ybco2kWws9N-7S4sHD15mhoFn5uUZgYEP3gJwi9EEYyTv18XbfEIQ5hPCCJMEX4AhlgxnCJHpoJtRLjMh8o8rMGrbHUKYcSyH4HMGn5U_ZqtqZ3VyBwuLQ2i-kwtexSOcNZsQXdru4XtXYeG3ymtrOiY5v4HKm253cDH4vfVJNfDFNv2d4NtrcFmrprU35z4Grw_Fer7IlqvHp_lsmWky5SmjBhnNNSN1ZRXjAlNDZF1RY7maco6rXGlFWI2U0DqnmrK8ojlTojZIa8PpGODTXR1D20Zbl1_R7bvwJUZl76bs3ZS9m_LspmPEH0a7pPrYKSrX_EvenUhnrf39JJBEUnD6A_GMdbw
CODEN	ITEVF5
CitedBy_id	crossref_primary_10_1016_j_knosys_2017_06_021 crossref_primary_10_1109_MCI_2019_2919398 crossref_primary_10_1109_ACCESS_2020_2989219 crossref_primary_10_1109_TCYB_2021_3082200 crossref_primary_10_1109_TCYB_2020_3008697 crossref_primary_10_1109_TCYB_2021_3081357 crossref_primary_10_1109_TEVC_2018_2875430 crossref_primary_10_1109_ACCESS_2019_2914115 crossref_primary_10_1016_j_asoc_2019_105879 crossref_primary_10_1016_j_asoc_2019_105911 crossref_primary_10_1016_j_ins_2021_03_008 crossref_primary_10_1016_j_knosys_2023_110801 crossref_primary_10_1109_MCI_2017_2742869 crossref_primary_10_1109_TSMC_2023_3298690 crossref_primary_10_1016_j_ins_2018_05_009 crossref_primary_10_1007_s11277_017_4653_x crossref_primary_10_1016_j_isatra_2020_10_065 crossref_primary_10_1016_j_simpat_2024_102917 crossref_primary_10_1109_TEVC_2016_2598687 crossref_primary_10_1109_MCI_2017_2742868 crossref_primary_10_1007_s11831_020_09415_3 crossref_primary_10_1016_j_ins_2016_09_026 crossref_primary_10_1371_journal_pone_0234625 crossref_primary_10_1016_j_swevo_2022_101107 crossref_primary_10_1109_TEVC_2016_2622301 crossref_primary_10_3390_pr7060361 crossref_primary_10_1109_TEVC_2021_3095481 crossref_primary_10_1007_s10489_022_03561_w crossref_primary_10_1007_s11047_017_9633_2 crossref_primary_10_1002_cpe_5342 crossref_primary_10_1007_s10489_020_02053_z crossref_primary_10_1007_s40747_021_00273_5 crossref_primary_10_1109_TCYB_2015_2490738 crossref_primary_10_1109_TEVC_2019_2933444 crossref_primary_10_1109_TEVC_2016_2519378 crossref_primary_10_1109_TEVC_2017_2769108 crossref_primary_10_1109_TEVC_2016_2600642 crossref_primary_10_1016_j_ins_2018_01_038 crossref_primary_10_1016_j_eswa_2021_116304 crossref_primary_10_1016_j_asoc_2018_12_011 crossref_primary_10_1109_ACCESS_2020_2991752 crossref_primary_10_1016_j_ins_2019_08_069 crossref_primary_10_1016_j_swevo_2024_101574 crossref_primary_10_1111_exsy_13709 crossref_primary_10_3233_JIFS_202724 crossref_primary_10_1109_TEVC_2016_2587808 crossref_primary_10_1109_TEVC_2018_2794319 crossref_primary_10_1016_j_ins_2021_08_054 crossref_primary_10_1007_s10586_022_03647_7 crossref_primary_10_1109_ACCESS_2020_3014871 crossref_primary_10_1109_TEVC_2016_2611642 crossref_primary_10_1016_j_knosys_2021_107430 crossref_primary_10_1109_TEVC_2018_2866927 crossref_primary_10_1007_s00500_018_3642_7 crossref_primary_10_1109_TCYB_2020_2971638 crossref_primary_10_1109_TCYB_2022_3180214 crossref_primary_10_1109_TEVC_2020_2999100 crossref_primary_10_1109_TSMC_2021_3069986 crossref_primary_10_1007_s12293_025_00458_2 crossref_primary_10_1109_TCYB_2019_2918087 crossref_primary_10_1002_int_22476 crossref_primary_10_1007_s10489_021_02707_6 crossref_primary_10_1007_s13748_019_00174_2 crossref_primary_10_1109_TCYB_2018_2884083 crossref_primary_10_1007_s12293_017_0240_7 crossref_primary_10_1007_s12293_016_0206_1 crossref_primary_10_1016_j_comcom_2023_10_020 crossref_primary_10_1016_j_swevo_2019_100605 crossref_primary_10_1109_TEVC_2018_2791283 crossref_primary_10_1109_TEVC_2022_3145582 crossref_primary_10_1016_j_ins_2018_03_015 crossref_primary_10_1016_j_kjs_2023_02_009 crossref_primary_10_1080_0305215X_2023_2283038 crossref_primary_10_1016_j_eswa_2022_117487 crossref_primary_10_1109_ACCESS_2017_2751071 crossref_primary_10_1007_s10489_017_0929_9 crossref_primary_10_1016_j_ins_2019_03_040 crossref_primary_10_1109_TEVC_2015_2501315 crossref_primary_10_1016_j_swevo_2019_05_011 crossref_primary_10_1016_j_asoc_2022_108798 crossref_primary_10_1109_TPWRD_2021_3107534 crossref_primary_10_3390_en15051884 crossref_primary_10_1007_s10489_017_0958_4 crossref_primary_10_1109_TEVC_2022_3144880 crossref_primary_10_1016_j_asoc_2023_111073 crossref_primary_10_1109_ACCESS_2019_2954542 crossref_primary_10_1109_TCYB_2022_3151793 crossref_primary_10_1109_TCYB_2021_3125362 crossref_primary_10_1109_TSMC_2022_3143657 crossref_primary_10_1016_j_ins_2020_03_104 crossref_primary_10_1016_j_ins_2024_120940 crossref_primary_10_1109_TEVC_2016_2549267 crossref_primary_10_1016_j_ins_2022_05_119 crossref_primary_10_1016_j_swevo_2019_100641 crossref_primary_10_1016_j_ins_2023_119559 crossref_primary_10_1007_s00521_021_05939_2 crossref_primary_10_1007_s00500_015_1955_3 crossref_primary_10_1109_TETCI_2018_2849380 crossref_primary_10_1016_j_ins_2019_05_083 crossref_primary_10_1007_s12065_022_00787_y crossref_primary_10_1016_j_asoc_2019_02_026 crossref_primary_10_1109_ACCESS_2020_3043253 crossref_primary_10_1007_s10489_022_03353_2 crossref_primary_10_1093_jcde_qwae022 crossref_primary_10_4018_IJSIR_323422
Cites_doi	10.1007/978-3-540-70928-2_56 10.1109/CEC.2005.1554688 10.1109/TSMCB.2008.926329 10.1109/TEVC.2013.2262178 10.1007/1-84628-137-7_6 10.1109/TEVC.2013.2240687 10.1162/EVCO_a_00009 10.1016/j.cor.2012.01.001 10.1109/TEVC.2010.2093579 10.1109/TEVC.2012.2227145 10.1007/978-3-540-70928-2_55 10.1007/978-3-540-70928-2_5 10.1109/CEC.2001.934293 10.1109/TEVC.2013.2243455 10.1109/TEVC.2007.910138 10.1109/NAFIPS.2002.1018061 10.1109/TEVC.2008.925798 10.1109/TEVC.2008.2009032 10.1109/TEVC.2014.2378512 10.1109/TEVC.2007.892759 10.1109/CEC.2008.4631121 10.1109/TEVC.2009.2027357 10.1109/TEVC.2010.2058117 10.1007/s10589-014-9644-1 10.1145/1389095.1389228 10.1007/3-540-44719-9_11 10.1109/TEVC.2013.2258025 10.1109/CEC.2009.4983256 10.1162/106365605774666895 10.1007/978-3-642-05258-3_56 10.1145/2330163.2330235 10.1109/CEC.2003.1299427 10.1109/TEVC.2005.861417 10.1109/TEVC.2003.810758 10.1109/TSMCB.2010.2068046 10.1007/s00500-014-1234-8 10.1109/CEC.2013.6557783 10.1109/TEVC.2012.2204264 10.1109/CEC.2007.4424985 10.1109/TEVC.2010.2098411 10.1016/j.tcs.2011.03.012 10.1109/TEVC.2013.2281535 10.1109/4235.996017
ContentType	Journal Article
DBID	97E RIA RIE AAYXX CITATION
DOI	10.1109/TEVC.2015.2424921
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef
DatabaseTitle	CrossRef
DatabaseTitleList
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	605
ExternalDocumentID	10_1109_TEVC_2015_2424921 7090975
Genre	orig-research
GrantInformation_xml	– fundername: Program for New Century Excellent Talents in University grantid: NCET-11-0715 funderid: 10.13039/501100004602 – fundername: Southwest Jiaotong University (SWJTU) grantid: SWJTU12CX008 funderid: 10.13039/501100004536 – fundername: 2014 Doctoral Innovation Funds of SWJTU – fundername: National Natural Science Foundation of China grantid: 61170016; 61373047 funderid: 10.13039/501100001809
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
ID	FETCH-LOGICAL-c265t-3d0dc5c42fbea45713d29fb3de5a6551b8aca24f0a7cc83c348b384a7fd0ccd53
IEDL.DBID	RIE
ISICitedReferencesCount	125
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000358883500009&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	Tue Nov 18 22:21:55 EST 2025 Sat Nov 29 03:13:47 EST 2025 Tue Aug 26 16:40:05 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
Keywords	many-objective optimization problem (MaOP) many-objective evolutionary algorithm (MaOEA) Directional diversity (DD) favorable convergence (FC)
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c265t-3d0dc5c42fbea45713d29fb3de5a6551b8aca24f0a7cc83c348b384a7fd0ccd53
PageCount	14
ParticipantIDs	crossref_citationtrail_10_1109_TEVC_2015_2424921 ieee_primary_7090975 crossref_primary_10_1109_TEVC_2015_2424921
PublicationCentury	2000
PublicationDate	2015-Aug. 2015-8-00
PublicationDateYYYYMMDD	2015-08-01
PublicationDate_xml	– month: 08 year: 2015 text: 2015-Aug.
PublicationDecade	2010
PublicationTitle	IEEE transactions on evolutionary computation
PublicationTitleAbbrev	TEVC
PublicationYear	2015
Publisher	IEEE
Publisher_xml	– name: IEEE
References	ref13 ref12 deb (ref47) 1995; 9 ref15 zitzler (ref20) 2004 ref14 ref11 ref10 ref17 villalobos (ref23) 2012 ref19 ref18 deb (ref48) 1996; 26 ref51 ref50 ref45 zitzler (ref46) 1998 ref41 ref44 ref43 ref49 steuer (ref42) 1986 ref8 ref9 ref4 zitzler (ref2) 2001 jain (ref40) 2013 ref3 ref6 ref5 zou (ref16) 2008; 38 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref1 ref39 ref38 ref24 ref26 ref25 ref22 ref21 deb (ref7) 2006 ref28 ref27 ref29
References_xml	– ident: ref30 doi: 10.1007/978-3-540-70928-2_56 – ident: ref29 doi: 10.1109/CEC.2005.1554688 – volume: 9 start-page: 115 year: 1995 ident: ref47 article-title: Simulated binary crossover for continuous search space publication-title: Complex Syst – volume: 38 start-page: 1402 year: 2008 ident: ref16 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: ref39 doi: 10.1109/TEVC.2013.2262178 – ident: ref43 doi: 10.1007/1-84628-137-7_6 – ident: ref50 doi: 10.1109/TEVC.2013.2240687 – ident: ref21 doi: 10.1162/EVCO_a_00009 – ident: ref35 doi: 10.1016/j.cor.2012.01.001 – ident: ref9 doi: 10.1109/TEVC.2010.2093579 – ident: ref13 doi: 10.1109/TEVC.2012.2227145 – ident: ref36 doi: 10.1007/978-3-540-70928-2_55 – ident: ref12 doi: 10.1007/978-3-540-70928-2_5 – ident: ref10 doi: 10.1109/CEC.2001.934293 – ident: ref17 doi: 10.1109/TEVC.2013.2243455 – ident: ref28 doi: 10.1109/TEVC.2007.910138 – ident: ref3 doi: 10.1109/NAFIPS.2002.1018061 – ident: ref32 doi: 10.1109/TEVC.2008.925798 – ident: ref51 doi: 10.1109/TEVC.2008.2009032 – volume: 26 start-page: 30 year: 1996 ident: ref48 article-title: A combined genetic adaptive search (GeneAS) for engineering design publication-title: Comput Sci Inf – ident: ref19 doi: 10.1109/TEVC.2014.2378512 – ident: ref26 doi: 10.1109/TEVC.2007.892759 – start-page: 307 year: 2013 ident: ref40 article-title: An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization publication-title: Proc EMO – ident: ref5 doi: 10.1109/CEC.2008.4631121 – year: 2001 ident: ref2 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization – ident: ref41 doi: 10.1109/TEVC.2009.2027357 – start-page: 832 year: 2004 ident: ref20 article-title: Indicator-based selection in multiobjective search publication-title: Proc PPSN – ident: ref37 doi: 10.1109/TEVC.2010.2058117 – ident: ref6 doi: 10.1007/s10589-014-9644-1 – ident: ref8 doi: 10.1145/1389095.1389228 – ident: ref15 doi: 10.1007/3-540-44719-9_11 – ident: ref4 doi: 10.1109/TEVC.2013.2258025 – start-page: 425 year: 1986 ident: ref42 publication-title: Multiple Criteria Optimization Theory Computation and Application – ident: ref33 doi: 10.1109/CEC.2009.4983256 – ident: ref11 doi: 10.1162/106365605774666895 – start-page: 292 year: 1998 ident: ref46 article-title: Multiobjective optimization using evolutionary algorithms-A comparative case study publication-title: Proc Parallel Prob Solving Nat – ident: ref14 doi: 10.1007/978-3-642-05258-3_56 – start-page: 505 year: 2012 ident: ref23 article-title: A new multi-objective evolutionary algorithm based on a performance assessment indicator publication-title: Proc GECCO doi: 10.1145/2330163.2330235 – ident: ref25 doi: 10.1109/CEC.2003.1299427 – ident: ref44 doi: 10.1109/TEVC.2005.861417 – ident: ref45 doi: 10.1109/TEVC.2003.810758 – ident: ref27 doi: 10.1109/TSMCB.2010.2068046 – ident: ref34 doi: 10.1007/s00500-014-1234-8 – ident: ref22 doi: 10.1109/CEC.2013.6557783 – ident: ref18 doi: 10.1109/TEVC.2012.2204264 – start-page: 3352 year: 2006 ident: ref7 article-title: Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems publication-title: Proc IEEE CEC – ident: ref31 doi: 10.1109/CEC.2007.4424985 – ident: ref49 doi: 10.1109/TEVC.2010.2098411 – ident: ref24 doi: 10.1016/j.tcs.2011.03.012 – ident: ref38 doi: 10.1109/TEVC.2013.2281535 – ident: ref1 doi: 10.1109/4235.996017
SSID	ssj0014519
Score	2.5033367
Snippet	Multiobjective evolutionary algorithms have become prevalent and efficient approaches for solving multiobjective optimization problems. However, their...
SourceID	crossref ieee
SourceType	Enrichment Source Index Database Publisher
StartPage	592
SubjectTerms	Algorithm design and analysis Convergence directional diversity Evolutionary computation favorable convergence Maintenance engineering many- objective evolutionary algorithm Many-objective optimization problem Optimization Sociology Statistics
Title	A Many-Objective Evolutionary Algorithm With Enhanced Mating and Environmental Selections
URI	https://ieeexplore.ieee.org/document/7090975
Volume	19
WOSCitedRecordID	wos000358883500009&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/eLvHCXMwlV3PS8MwFH7o8DAPTqfi_EUOnsRuWZM2yXFIhwd_gVPnqaRJ6pTZSZmC_71JW-sEEbyUUhII-V6Sl773vg_gSPCEJ0xgT1KhPaoY9hxLmheSfkpTkvqqEO27O2eXl3w8FtdLcFLXwhhjiuQz03WvRSxfz9Sb-1XWY1hgwYJlWGYsLGu16oiBo0kpk-mF9Rj5uIpg9rHojaK7U5fEFXRdLYTw-z_OoAVRleJMGbb-N5p1WKt8RzQowd6AJZO1ofWly4CqZdqG1QWSwTY0nT9Z0jFvwsMAXdjl710lz-VOh6L3yvhk_oEG08dZ_jSfvKB7-0RRNikSBGwflx2NZKZR9F0ZZ8dyU8joONPdgtthNDo98yp1BU_5YTD3iMZaBYr6aWIkDexlVfsiTYg2gQytH5VwqaRPUyyZUpwoQnlCOJUs1VgpHZBtaGSzzOwAUsLnhoZhyhSm2s6KZgkNtR9KbAQJaAfw13zHqqIedwoY07i4gmARO4hiB1FcQdSB47rLa8m78VfjTQdP3bBCZvf3z3vQdJ3LLL59aMzzN3MAK-rdYpEfFlb1CQ3ryp0
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bS8MwFD7oFNQHL1Pxbh58EuuyJm2SxyEVxTkFp86nkiapF7STsQn-e5M2TgURfCmlJCXkO01Oes75PoA9wTOeMYEDSYUOqGI4cCxpQUyaOc1JHqpStO-mzTod3uuJywk4GNfCGGPK5DNz6G7LWL7uq5H7VdZgWGDBokmYcspZvlprHDNwRClVOr2wPiPv-RhmE4tGN7k5cmlc0aGrhhBh88cu9E1WpdxVjhf-N55FmPfeI2pVcC_BhCnqsPCpzID8h1qHuW80g3WYdR5lRci8DHctdG4XgOAie6rWOpS8efOTg3fUer7vDx6HDy_o1l5RUjyUKQK2j8uPRrLQKPmqjbNjuSqFdJzxrsD1cdI9Ogm8vkKgwjgaBkRjrSJFwzwzkkb2uKpDkWdEm0jG1pPKuFQypDmWTClOFKE8I5xKlmuslI7IKtSKfmHWACkRckPjOGcKU21nRbOMxjqMJTaCRHQd8Od8p8qTjzsNjOe0PIRgkTqIUgdR6iFah_1xl9eKeeOvxssOnnFDj8zG7493Yeake95O26eds02YdS-qcvq2oDYcjMw2TKs3i8tgp7SwD8UIzeY
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+Many-Objective+Evolutionary+Algorithm+With+Enhanced+Mating+and+Environmental+Selections&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Jixiang+Cheng&rft.au=Yen%2C+Gary+G.&rft.au=Gexiang+Zhang&rft.date=2015-08-01&rft.pub=IEEE&rft.issn=1089-778X&rft.volume=19&rft.issue=4&rft.spage=592&rft.epage=605&rft_id=info:doi/10.1109%2FTEVC.2015.2424921&rft.externalDocID=7090975
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