An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization

In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy envi...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IEEE transactions on evolutionary computation Ročník 11; číslo 3; s. 354 - 381
Hlavní autoři:	Goh, C.K., Tan, K.C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.06.2007 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Applied sciences Archiving Artificial intelligence Computer science; control theory; systems Constraint optimization Convergence Distortion Evolutionary Evolutionary algorithms Evolutionary algorithms (EAs) Evolutionary computation Exact sciences and technology Learning and adaptive systems multiobjective optimization Noise measurement Noise robustness noisy fitness function Optimization Parallel processing Search methods Searching Signal distortion Stochastic processes Strategy Studies Working environment noise Discontinuity Local search Probabilistic approach Evolutionary algorithm Empirical method Multiobjective programming Optimization multiobjective optimization Environment impact Imperfect information Evolutionary algorithms (EAs) Robustness noisy fitness function Signal distortion Signal to noise ratio
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity
AbstractList	Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity.
Author	Tan, K.C. Goh, C.K.
Author_xml	– sequence: 1 givenname: C.K. surname: Goh fullname: Goh, C.K. organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore – sequence: 2 givenname: K.C. surname: Tan fullname: Tan, K.C. organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18805599$$DView record in Pascal Francis
BookMark	eNp9kc1r3DAQxUVIIR_tvZCLCSQ9eTsjy_o4hmXbBpIGSlp6E4pWDlq80kayF9K_vnI2JJBDYGDm8HtvmHlHZD_E4Aj5jDBDBPX1dvFnPqMAfCYlZVTukUNUDGsAyvfLDFLVQsi_B-Qo5xUAshbVIfl1EarLsHV58Pdm8DFUpX5Gnx-rRdj6FMPahSFXPlSLbezHCTHpsboe-zLerZwd_NZVN5vBr_2_J4eP5ENn-uw-Pfdj8vvb4nb-o766-X45v7iqLaPtUBsHKBrsjOXSIpUtFxKUWqql421nqDWItlHYgBNNJ-UdX8qu4y0TglFmmuaYfNn5blJ8GMsFeu2zdX1vgotj1lIC50zRiTx_l2xYSxUIVcDTN-AqjimUK7TkDASiwAKdPUMmW9N3yQTrs94kvy6f0Vj2tq2azGDH2RRzTq57RUBPmekpMz1lpneZFQl_I7F-eHrqkIzv3xOe7ITeOfeyh1EKXPDmP5f9pVA
CODEN	ITEVF5
CitedBy_id	crossref_primary_10_1109_TCYB_2014_2310651 crossref_primary_10_1109_TETCI_2023_3336918 crossref_primary_10_1016_j_ins_2022_05_050 crossref_primary_10_1109_TCYB_2016_2520477 crossref_primary_10_1016_j_ins_2013_06_037 crossref_primary_10_1007_s40747_021_00270_8 crossref_primary_10_1007_s11277_024_11415_6 crossref_primary_10_1016_j_eswa_2010_02_008 crossref_primary_10_1016_j_ijleo_2013_05_101 crossref_primary_10_1109_TEVC_2010_2051443 crossref_primary_10_1109_TSMC_2019_2943973 crossref_primary_10_1016_j_neucom_2023_126761 crossref_primary_10_1016_j_engappai_2025_111676 crossref_primary_10_1016_j_asoc_2023_110631 crossref_primary_10_1109_TCYB_2015_2510698 crossref_primary_10_1109_TEVC_2013_2248159 crossref_primary_10_1016_j_asoc_2024_111600 crossref_primary_10_1016_j_future_2018_06_008 crossref_primary_10_1049_iet_ipr_2020_0535 crossref_primary_10_1109_TEVC_2016_2639360 crossref_primary_10_1007_s10710_012_9164_7 crossref_primary_10_1007_s40747_022_00717_6 crossref_primary_10_1007_s10732_008_9077_z crossref_primary_10_1016_j_asoc_2018_12_031 crossref_primary_10_1007_s10589_014_9717_1 crossref_primary_10_1016_j_asoc_2019_03_042 crossref_primary_10_1109_TEVC_2013_2244898 crossref_primary_10_1162_evco_a_00170 crossref_primary_10_1109_TEVC_2008_920671 crossref_primary_10_1016_j_ejor_2009_11_003 crossref_primary_10_1109_TCYB_2022_3151974 crossref_primary_10_1109_TEVC_2008_925798 crossref_primary_10_1007_s12293_009_0029_4 crossref_primary_10_1016_j_swevo_2017_08_004 crossref_primary_10_1109_TCYB_2019_2909806 crossref_primary_10_1016_j_swevo_2024_101468 crossref_primary_10_1016_j_eswa_2025_129325 crossref_primary_10_1016_j_neucom_2023_03_073 crossref_primary_10_1007_s00158_021_02852_z crossref_primary_10_1080_00207721_2011_598964 crossref_primary_10_1109_TEVC_2010_2046666 crossref_primary_10_1016_j_ins_2018_10_005 crossref_primary_10_1109_TSMCB_2012_2209115 crossref_primary_10_1016_j_swevo_2011_03_001 crossref_primary_10_1002_2017WR022068 crossref_primary_10_1109_TEVC_2014_2304415 crossref_primary_10_1109_TEVC_2009_2021467 crossref_primary_10_1109_TEVC_2023_3250350 crossref_primary_10_1109_TEVC_2016_2574621 crossref_primary_10_1016_j_ejor_2009_05_005 crossref_primary_10_1145_3300148 crossref_primary_10_1162_EVCO_a_00066 crossref_primary_10_1109_TFUZZ_2018_2848261 crossref_primary_10_1002_mcda_1486 crossref_primary_10_1080_00207721_2011_618645 crossref_primary_10_1016_j_swevo_2023_101259 crossref_primary_10_1093_jcde_qwac130 crossref_primary_10_1109_TEVC_2019_2896967 crossref_primary_10_1007_s13369_020_05147_5 crossref_primary_10_1016_j_artint_2015_06_004 crossref_primary_10_1109_TCYB_2018_2881227 crossref_primary_10_1016_j_swevo_2024_101492 crossref_primary_10_3846_tede_2019_10291 crossref_primary_10_1109_TEVC_2018_2872453 crossref_primary_10_1016_j_swevo_2016_09_002 crossref_primary_10_3390_math10060943 crossref_primary_10_1016_j_advwatres_2017_03_023 crossref_primary_10_1007_s11783_017_0934_6 crossref_primary_10_1007_s40815_023_01558_2 crossref_primary_10_1016_j_ins_2019_09_060 crossref_primary_10_2478_jee_2022_0017 crossref_primary_10_1109_TSMC_2024_3357872 crossref_primary_10_1007_s12293_009_0012_0 crossref_primary_10_1016_j_jhydrol_2024_132576 crossref_primary_10_1109_TCYB_2021_3059252 crossref_primary_10_1109_TEVC_2009_2033586 crossref_primary_10_1109_TEVC_2022_3233364 crossref_primary_10_1016_j_advwatres_2011_10_011 crossref_primary_10_1016_j_camwa_2012_02_051 crossref_primary_10_1109_TFUZZ_2020_2979119 crossref_primary_10_1007_s11047_014_9415_z crossref_primary_10_1007_s12293_015_0175_9 crossref_primary_10_1109_TCYB_2014_2307319 crossref_primary_10_1007_s13042_017_0695_3 crossref_primary_10_1016_j_ejor_2023_08_023 crossref_primary_10_3390_su13010410 crossref_primary_10_1007_s00500_016_2076_3 crossref_primary_10_1016_j_ins_2019_01_066 crossref_primary_10_1016_j_knosys_2018_05_043 crossref_primary_10_1007_s00500_013_1085_8 crossref_primary_10_1109_TEVC_2019_2958075 crossref_primary_10_1016_j_swevo_2023_101420 crossref_primary_10_1038_s41598_024_68436_1 crossref_primary_10_1109_TEVC_2024_3438115 crossref_primary_10_1007_s00500_018_3356_x crossref_primary_10_1016_j_swevo_2016_12_005
Cites_doi	10.1002/j.1538-7305.1948.tb01338.x 10.1162/106365604773955139 10.1016/S0933-3657(02)00014-3 10.1162/evco.1999.7.3.205 10.1109/TEVC.2003.810760 10.1016/j.ejor.2004.08.038 10.1109/CEC.2002.1007013 10.1109/CEC.2003.1299404 10.1109/TSMCB.2002.804372 10.1016/S0045-7825(99)00386-2 10.1613/jair.842 10.1109/4235.974840 10.1007/3-540-44719-9_23 10.1007/978-1-4684-5287-7 10.1016/S0377-2217(98)00262-8 10.1109/CEC.2000.870296 10.1109/TEVC.2003.812220 10.1017/S0890060401020054 10.1109/CEC.2000.870274 10.1109/TEVC.2003.810758 10.1162/106365600568202 10.1109/TSMCA.2004.824873 10.1109/TEVC.2003.810761 10.1162/evco.1994.2.3.221 10.1109/CEC.2002.1006261 10.1109/4235.910466 10.1214/aoms/1177704472 10.1162/106365600568167 10.1109/4235.797969 10.1007/BF03325101 10.1109/4235.996017 10.1109/ICEC.1994.350045 10.1109/TAES.2004.1310019 10.1109/CEC.2000.870756 10.1109/CEC.2002.1007024 10.1109/4235.735433 10.1109/4235.985690 10.1109/TEVC.2003.810759 10.1007/3-540-44719-9_22 10.1145/298151.298382 10.1023/A:1015516501242 10.1109/CEC.2001.934294 10.1016/S0165-0114(01)00161-0 10.1007/s10462-004-2902-3
ContentType	Journal Article
Copyright	2007 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007
Copyright_xml	– notice: 2007 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007
DBID	97E RIA RIE AAYXX CITATION IQODW 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
DOI	10.1109/TEVC.2006.882428
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Pascal-Francis 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 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 Computer Science Applied Sciences
EISSN	1941-0026
EndPage	381
ExternalDocumentID	2333530511 18805599 10_1109_TEVC_2006_882428 4220676
Genre	orig-research
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 IQODW RIG 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3
ID	FETCH-LOGICAL-c425t-ae01731fac68c1285678099d9de65fa2ca11c39130e73f88b6d8ff65477424a33
IEDL.DBID	RIE
ISICitedReferencesCount	149
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000247044400007&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	Sat Sep 27 23:08:56 EDT 2025 Sat Sep 27 21:40:05 EDT 2025 Sun Nov 30 03:54:57 EST 2025 Mon Jul 21 09:13:24 EDT 2025 Tue Nov 18 21:25:31 EST 2025 Sat Nov 29 03:13:45 EST 2025 Tue Aug 26 16:43:48 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	Discontinuity Local search Probabilistic approach Evolutionary algorithm Empirical method Multiobjective programming Optimization multiobjective optimization Environment impact Imperfect information Evolutionary algorithms (EAs) Robustness noisy fitness function Signal distortion Signal to noise ratio
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c425t-ae01731fac68c1285678099d9de65fa2ca11c39130e73f88b6d8ff65477424a33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
OpenAccessLink	http://scholarbank.nus.edu.sg/handle/10635/55042
PQID	864071171
PQPubID	23500
PageCount	28
ParticipantIDs	proquest_miscellaneous_880664923 proquest_miscellaneous_34529079 pascalfrancis_primary_18805599 proquest_journals_864071171 crossref_citationtrail_10_1109_TEVC_2006_882428 ieee_primary_4220676 crossref_primary_10_1109_TEVC_2006_882428
PublicationCentury	2000
PublicationDate	2007-06-01
PublicationDateYYYYMMDD	2007-06-01
PublicationDate_xml	– month: 06 year: 2007 text: 2007-06-01 day: 01
PublicationDecade	2000
PublicationPlace	New York, NY
PublicationPlace_xml	– name: New York, NY – name: New York
PublicationTitle	IEEE transactions on evolutionary computation
PublicationTitleAbbrev	TEVC
PublicationYear	2007
Publisher	IEEE Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: Institute of Electrical and Electronics Engineers – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref57 ref13 ref15 ref14 ref53 ref52 ref55 ref54 ref17 ref16 branke (ref6) 2001 rana (ref33) 1996 zitzler (ref56) 2001 giraud-carrier (ref8) 2000 ref51 ref50 singh (ref41) 2003 ref46 ref45 ref48 ref42 ref44 goldberg (ref19) 1987 ref43 ref49 coello (ref10) 2002; 16 ref7 ref4 ref3 ref5 ref40 fonseca (ref18) 1993 ref35 goldberg (ref20) 2002 scott (ref39) 1995 ref37 rudolph (ref36) 2001; 1 ref31 ref30 coello (ref9) 1999; 1 rattray (ref34) 1997 ref32 deb (ref12) 1989 ref2 ref1 ref38 tan (ref47) 2003; 18 ref24 corne (ref11) 2000 ref26 ref25 ref22 ref21 ref28 ref27 ref29 hughes (ref23) 2001; 2
References_xml	– ident: ref40 doi: 10.1002/j.1538-7305.1948.tb01338.x – ident: ref15 doi: 10.1162/106365604773955139 – volume: 1 start-page: 318 year: 2001 ident: ref36 article-title: a partial order approach to noisy fitness functions publication-title: Proc Congr Evol Comput – ident: ref46 doi: 10.1016/S0933-3657(02)00014-3 – ident: ref13 doi: 10.1162/evco.1999.7.3.205 – year: 2002 ident: ref20 publication-title: The Design of Innovation Lessons from and for Competent Genetic Algorithms – ident: ref53 doi: 10.1109/TEVC.2003.810760 – ident: ref48 doi: 10.1016/j.ejor.2004.08.038 – ident: ref27 doi: 10.1109/CEC.2002.1007013 – ident: ref49 doi: 10.1109/CEC.2003.1299404 – ident: ref7 doi: 10.1109/TSMCB.2002.804372 – start-page: 41 year: 1987 ident: ref19 article-title: genetic algorithms with sharing for multi-modal function optimization publication-title: Proc 2nd Int Conf Genetic Algorithms – ident: ref4 doi: 10.1016/S0045-7825(99)00386-2 – volume: 18 start-page: 183 year: 2003 ident: ref47 article-title: an evolutionary algorithm with advanced goal and priority specification for multi-objective optimization publication-title: J Artif Intell Res doi: 10.1613/jair.842 – ident: ref44 doi: 10.1109/4235.974840 – ident: ref22 doi: 10.1007/3-540-44719-9_23 – ident: ref16 doi: 10.1007/978-1-4684-5287-7 – ident: ref21 doi: 10.1016/S0377-2217(98)00262-8 – start-page: 839 year: 2000 ident: ref11 article-title: the pareto envelope-based selection algorithm for multiobjective optimization publication-title: Proc 6th Int Conf Parallel Problem Solving From Nature (PPSN VI) – ident: ref52 doi: 10.1109/CEC.2000.870296 – ident: ref30 doi: 10.1109/TEVC.2003.812220 – volume: 16 start-page: 39 year: 2002 ident: ref10 publication-title: Artificial Intelligence in Eng Design Analysis and Manufacturing doi: 10.1017/S0890060401020054 – ident: ref29 doi: 10.1109/CEC.2000.870274 – ident: ref57 doi: 10.1109/TEVC.2003.810758 – start-page: 416 year: 1993 ident: ref18 article-title: genetic algorithm for multiobjective optimization, formulation, discussion and generalization publication-title: Proc 5th Int Conf Genetic Algorithms – year: 1995 ident: ref39 publication-title: Fault tolerant design using single and multi-criteria genetic algorithms – ident: ref55 doi: 10.1162/106365600568202 – ident: ref17 doi: 10.1109/TSMCA.2004.824873 – ident: ref5 doi: 10.1109/TEVC.2003.810761 – start-page: 243 year: 2001 ident: ref6 article-title: efficient fitness estimation in noisy environments publication-title: Proc Genetic Evolutionary Computatation Conf – ident: ref38 doi: 10.1162/evco.1994.2.3.221 – start-page: 198 year: 1996 ident: ref33 article-title: searching in the presence of noise publication-title: Proc 4th Int Conf Parallel Problem Solving from Nature (PPSN IV) – ident: ref37 doi: 10.1109/CEC.2002.1006261 – ident: ref42 doi: 10.1109/4235.910466 – ident: ref32 doi: 10.1214/aoms/1177704472 – ident: ref26 doi: 10.1162/106365600568167 – start-page: 117 year: 1997 ident: ref34 publication-title: Foundations of Genetic Algorithms 4 – ident: ref54 doi: 10.1109/4235.797969 – volume: 1 start-page: 269 year: 1999 ident: ref9 article-title: a comprehensive survey of evolutionary-based multiobjective optimization techniques publication-title: Int J Knowl Inf Syst doi: 10.1007/BF03325101 – start-page: 42 year: 1989 ident: ref12 article-title: an investigation on niche and species formation in genetic function optimization publication-title: Proc 3rd Int Conf Genetic Algorithms – ident: ref14 doi: 10.1109/4235.996017 – ident: ref3 doi: 10.1109/ICEC.1994.350045 – ident: ref28 doi: 10.1109/TAES.2004.1310019 – ident: ref35 doi: 10.1109/CEC.2000.870756 – ident: ref2 doi: 10.1109/CEC.2002.1007024 – ident: ref31 doi: 10.1109/4235.735433 – start-page: 36 year: 2000 ident: ref8 article-title: unifying learning with evolution through baldwinian evolution and lamarckism: a case study publication-title: Proc Symp Comput Intell Learning – ident: ref1 doi: 10.1109/4235.985690 – ident: ref24 doi: 10.1109/TEVC.2003.810759 – ident: ref50 doi: 10.1007/3-540-44719-9_22 – ident: ref51 doi: 10.1145/298151.298382 – ident: ref45 doi: 10.1023/A:1015516501242 – year: 2001 ident: ref56 publication-title: SPEA2 Improving the strength Pareto evolutionary algorithm – volume: 2 start-page: 963 year: 2001 ident: ref23 article-title: constraint handling with uncertain and noisy multi-objective evolution publication-title: Proc 2001 Congr Evol Comput doi: 10.1109/CEC.2001.934294 – ident: ref43 doi: 10.1016/S0165-0114(01)00161-0 – ident: ref25 doi: 10.1007/s10462-004-2902-3 – year: 2003 ident: ref41 publication-title: Uncertainty based multi-objective optimization of groundwater remediation design
SSID	ssj0014519
Score	2.3088055
Snippet	In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in... Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator...
SourceID	proquest pascalfrancis crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	354
SubjectTerms	Algorithms Applied sciences Archiving Artificial intelligence Computer science; control theory; systems Constraint optimization Convergence Distortion Evolutionary Evolutionary algorithms Evolutionary algorithms (EAs) Evolutionary computation Exact sciences and technology Learning and adaptive systems multiobjective optimization Noise measurement Noise robustness noisy fitness function Optimization Parallel processing Search methods Searching Signal distortion Stochastic processes Strategy Studies Working environment noise
Title	An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
URI	https://ieeexplore.ieee.org/document/4220676 https://www.proquest.com/docview/864071171 https://www.proquest.com/docview/34529079 https://www.proquest.com/docview/880664923
Volume	11
WOSCitedRecordID	wos000247044400007&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/eLvHCXMwlV1LT-MwEB4B4rAclrfI8vJhL0gbmjSNHR8RKuJUEIJVb5Hj2FIRJKhpkfrvdyZOskU8JKQcImWcWB6PZybz-AB-K2PsQKncj5WJfbRvtZ-hK-Tzfob6NpeBtg5sQoxGyXgsb1fgT1cLY4ypk8_MOd3Wsfy81HP6VdYb9KnZOF-FVSGEq9XqIgbUJsUl00u0GJNxG5IMZO9--PfShR3QnBwQ7vqSCqoxVSgjUlW4KNahWbw7mGttc7X5vXluwc_GqmQXbhtsw4opdmCzRWxgjQDvwMZS-8FduLso2FKfjbJgeI3KSbVgw6X6NzYp2PC12aJqumB10W6ZPbqzkt3gqfPclHPuwcPV8P7y2m8wFnyN0jrzlUGRjEKrNE806qoYlRcajbnMDY-t6msVhjqSqOmMiGySZDxPrCXEYvSpByqK9mGtKAtzAAxts0xyKbRAlR_zTCFtX9NKGCvQB_eg1y57qpsG5ISD8ZTWjkggU2IU4WLy1DHKg7NuxItrvvEF7S4xoqNreODByRvO_n8PHlzUbc2Dw5bVaSO-VZpQeDMMRejBafcU5Y6CKaow5bxKI4pYBwLHs08o8AOcU_-7Xx9P7RB-tMmHQXgEa7Pp3BzDun6dTarpSb27_wH2qviP
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB42aSDNoXmWuNsmOuRSiLN-ytYxhA0pSTalbMvejCxLsKW1y3o3kH_fGct2N_QBBR8MHtlCo9HMeB4fwJnU2kRSFm4sdeyifavcHF0hlwc56ttCeMpYsIlkMklnM_FxAOd9LYzWukk-0xd028Tyi0qt6FfZKAqo2TjfgBdxFAW-rdbqYwbUKMWm0wu0GdNZF5T0xGg6_nJlAw9oUEaEvL6mhBpUFcqJlDUui7F4Fr8dzY2-ud79v5nuwavWrmSXdiPsw0CXB7DbYTawVoQPYGetAeEhfLos2VqnjapkeE2qef3ExmsVcGxesvFju0nl4ok1ZbtV_tWeluwBz53vbUHnEXy-Hk-vbtwWZcFVKK9LV2oUytA3UvFUobaKUX2h2ViIQvPYyEBJ31ehQF2nk9Ckac6L1BjCLEavOpJh-Bo2y6rUx8DQOssFF4lKUOnHPJdIGyhaCW0S9MIdGHXLnqm2BTkhYXzLGlfEExkxipAxeWYZ5cD7fsQP237jH7SHxIieruWBAyfPOPvrPXh0Ub81B4Ydq7NWgOsspQCn7ye-A6f9U5Q8CqfIUlerOgspZu0lOJ79hQI_wDl1wHvz56mdwvbN9P4uu_swuR3Cyy4V0fPfwuZysdLvYEs9Luf14qTZ6T8BA-b71g
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=An+Investigation+on+Noisy+Environments+in+Evolutionary+Multiobjective+Optimization&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Goh%2C+C+K&rft.au=Tan%2C+K+C&rft.date=2007-06-01&rft.issn=1089-778X&rft.volume=11&rft.issue=3&rft_id=info:doi/10.1109%2FTEVC.2006.882428&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