PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outso...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on cybernetics Jg. 54; H. 6; S. 3638 - 3651
Hauptverfasser:	Zhao, Bowen, Chen, Wei-Neng, Wei, Feng-Feng, Liu, Ximeng, Pei, Qingqi, Zhang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximation Cloud computing Combinatorial analysis Combinatorial optimization Datasets Encryption Evolutionary algorithms Evolutionary computation evolutionary computation as a service (ECaaS) Genetic algorithms Knapsack problem Optimization Privacy privacy protection secure computing Servers Social factors Statistics Traveling salesman problem
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.
AbstractList	Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution. Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement EAs for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution. EA, such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users lack the capability to implement evolutionary algorithms (EAs) for solving COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, however, it poses privacy concerns. To this end, this article proposes a novel computing paradigm called evolutionary computation as a service (ECaaS), where a cloud server renders evolutionary computation services for users while ensuring their privacy. Following the concept of ECaaS, this article presents privacy-preserving genetic algorithm (PEGA), a privacy-preserving GA designed specifically for COPs. PEGA enables users, regardless of their domain expertise or resource availability, to outsource COPs to the cloud server that holds a competitive GA and approximates the optimal solution while safeguarding privacy. Notably, PEGA features the following characteristics. First, PEGA empowers users without domain expertise or sufficient resources to solve COPs effectively. Second, PEGA protects the privacy of users by preventing the leakage of optimization problem details. Third, PEGA performs comparably to the conventional GA when approximating the optimal solution. To realize its functionality, we implement PEGA falling in a twin-server architecture and evaluate it on two widely known COPs: 1) the traveling Salesman problem (TSP) and 2) the 0/1 knapsack problem (KP). Particularly, we utilize encryption cryptography to protect users' privacy and carefully design a suite of secure computing protocols to support evolutionary operators of GA on encrypted chromosomes. Privacy analysis demonstrates that PEGA successfully preserves the confidentiality of COP contents. Experimental evaluation results on several TSP datasets and KP datasets reveal that PEGA performs equivalently to the conventional GA in approximating the optimal solution.
Author	Pei, Qingqi Chen, Wei-Neng Zhao, Bowen Liu, Ximeng Wei, Feng-Feng Zhang, Jun
Author_xml	– sequence: 1 givenname: Bowen orcidid: 0000-0001-9864-9729 surname: Zhao fullname: Zhao, Bowen email: bwinzhao@gmail.com organization: Guangzhou Institute of Technology, Xidian University, Guangzhou, China – sequence: 2 givenname: Wei-Neng orcidid: 0000-0003-0843-5802 surname: Chen fullname: Chen, Wei-Neng email: cwnraul634@aliyun.com organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Feng-Feng orcidid: 0009-0003-4708-8791 surname: Wei fullname: Wei, Feng-Feng organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 4 givenname: Ximeng orcidid: 0000-0002-4238-3295 surname: Liu fullname: Liu, Ximeng email: snbnix@gmail.com organization: College of Computer and Data Science, Fuzhou University, Fuzhou, Fujian, China – sequence: 5 givenname: Qingqi orcidid: 0000-0001-7601-5434 surname: Pei fullname: Pei, Qingqi email: qqpei@mail.xidian.edu.cn organization: School of Telecommunications Engineering, Xidian University, Xi'an, China – sequence: 6 givenname: Jun surname: Zhang fullname: Zhang, Jun organization: College of Artificial Intelligence, Nankai University, Tianjin, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38215330$$D View this record in MEDLINE/PubMed
BookMark	eNp9kcFuEzEQhi1UREvoAyAhtBIXLpvaHsdec0ujElArNYdy4GTZzmxxtbsOtlOpPD0bklZVD8xlRqPvH83M_5YcDXFAQt4zOmWM6rObxc_zKaccpgBCNhJekRPOZFNzrmZHT7VUx-Q05zs6RjO2dPOGHEPD2QyAnpDL1cVy_qWaV6sU7q1_qFcJM6b7MNxWSxywBF_Nu9uYQvnVV21M1SL2Lgy2jC3bVdebEvrwx5YQh3fkdWu7jKeHPCE_vl7cLL7VV9fL74v5Ve1hRkttqRdMOKaZ4MhBUOk8KKmp4q2ctdyvhdPSrRUTAIhSrxl3SjuBWnl0Gibk837uJsXfW8zF9CF77Do7YNxmwzXXFIAzOqKfXqB3cZuGcTsDVHIFEsY_TMjHA7V1Pa7NJoXepgfz-KYRUHvAp5hzwtb4UP7dXJINnWHU7DwxO0_MzhNz8GRUshfKx-H_03zYawIiPuNBN0I08Bcz3ZRI
CODEN	ITCEB8
CitedBy_id	crossref_primary_10_1007_s42979_025_04039_5 crossref_primary_10_1007_s40745_025_00642_8 crossref_primary_10_1109_MCI_2025_3563425 crossref_primary_10_1109_TCYB_2025_3573292 crossref_primary_10_1109_TCYB_2025_3579593 crossref_primary_10_1631_FITEE_2400513 crossref_primary_10_1016_j_knosys_2025_114419 crossref_primary_10_1109_ACCESS_2024_3471081 crossref_primary_10_1016_j_jwpe_2025_107784 crossref_primary_10_1007_s00607_025_01504_0 crossref_primary_10_1016_j_comnet_2025_111664 crossref_primary_10_1109_TETCI_2025_3529903 crossref_primary_10_1016_j_eswa_2025_127320 crossref_primary_10_1109_TIFS_2024_3402173 crossref_primary_10_1016_j_swevo_2025_102018 crossref_primary_10_1038_s41598_025_91140_7 crossref_primary_10_3390_agronomy14122987
Cites_doi	10.1109/TEVC.2022.3144419 10.32604/jcs.2020.09308 10.1145/1276958.1277214 10.1145/3411501.3419418 10.1109/TCYB.2022.3151234 10.1007/3-540-45682-1_20 10.1109/CIMCA.2005.1631619 10.1038/s41467-020-16175-y 10.1109/TCYB.2020.3005047 10.1109/TR.2023.3258983 10.1007/978-981-33-4543-0_57 10.1109/SFCS.1982.38 10.1007/978-3-031-36622-2_30 10.1007/3-540-48910-X_16 10.1109/SP.2017.12 10.1109/ICIST.2019.8836906 10.1023/A:1006529012972 10.1109/TEVC.2021.3080683 10.1109/TCYB.2020.2983860 10.1109/TCYB.2022.3224169 10.1002/9781118600283 10.1007/978-3-540-74553-2_38 10.1145/3158363 10.1007/978-3-642-15871-1_5 10.1504/IJACT.2014.062738 10.1109/tevc.2023.3319566 10.1109/JSTSP.2015.2429117 10.7551/mitpress/1090.001.0001 10.1109/MCI.2023.3327892 10.1109/tifs.2024.3402173 10.1007/978-3-662-03315-9_4 10.1145/3485832.3485842 10.1109/ICACI52617.2021.9435860 10.1145/3372297.3417274 10.1109/TIFS.2022.3211707
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
DBID	97E RIA RIE AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8
DOI	10.1109/TCYB.2023.3346863
DatabaseName	IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Aerospace Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed Aerospace Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Statistics Sciences (General)
EISSN	2168-2275
EndPage	3651
ExternalDocumentID	38215330 10_1109_TCYB_2023_3346863 10398448
Genre	orig-research Journal Article
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 62202358 funderid: 10.13039/501100001809 – fundername: National Research Foundation of Korea grantid: NRF2022H1D3A2A01093478 funderid: 10.13039/501100003725 – fundername: Guangdong Regional Joint Foundation Key Project grantid: 2022B1515120076 – fundername: China Postdoctoral Science Foundation grantid: 2023TQ0258 funderid: 10.13039/501100002858
GroupedDBID	0R~ 4.4 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD HZ~ IFIPE IPLJI JAVBF M43 O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM 7SC 7SP 7TB 8FD F28 FR3 H8D JQ2 L7M L~C L~D 7X8
ID	FETCH-LOGICAL-c350t-a0c414b19142e23406bc3769072f65f2cd4b96bd71433ee69d12b79b4e97ceb93
IEDL.DBID	RIE
ISICitedReferencesCount	22
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001174076900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2168-2267 2168-2275
IngestDate	Sun Sep 28 01:15:23 EDT 2025 Sun Jun 29 16:57:27 EDT 2025 Thu Apr 03 07:00:29 EDT 2025 Sat Nov 29 02:02:40 EST 2025 Tue Nov 18 21:41:07 EST 2025 Wed Aug 27 03:03:28 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c350t-a0c414b19142e23406bc3769072f65f2cd4b96bd71433ee69d12b79b4e97ceb93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-4238-3295 0009-0003-4708-8791 0000-0001-7601-5434 0000-0001-9864-9729 0000-0003-0843-5802
PMID	38215330
PQID	3062736353
PQPubID	85422
PageCount	14
ParticipantIDs	proquest_miscellaneous_2929033210 proquest_journals_3062736353 pubmed_primary_38215330 crossref_citationtrail_10_1109_TCYB_2023_3346863 crossref_primary_10_1109_TCYB_2023_3346863 ieee_primary_10398448
PublicationCentury	2000
PublicationDate	2024-06-01
PublicationDateYYYYMMDD	2024-06-01
PublicationDate_xml	– month: 06 year: 2024 text: 2024-06-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Piscataway
PublicationTitle	IEEE transactions on cybernetics
PublicationTitleAbbrev	TCYB
PublicationTitleAlternate	IEEE Trans Cybern
PublicationYear	2024
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	ref13 ref12 ref15 ref37 ref14 ref31 ref30 ref11 ref33 Goldberg (ref36) Gilad-Bachrach (ref5) ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 Mohassel (ref6) ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 Miller (ref34) 1995; 9 ref8 ref7 ref9 ref4 ref3 Whitley (ref35) ref40
References_xml	– ident: ref24 doi: 10.1109/TEVC.2022.3144419 – ident: ref20 doi: 10.32604/jcs.2020.09308 – start-page: 154 volume-title: Proc. 1st Int. Conf. Genet. Algorithms Appl. ident: ref36 article-title: Alleles, loci, and the traveling salesman problem – ident: ref9 doi: 10.1145/1276958.1277214 – ident: ref7 doi: 10.1145/3411501.3419418 – ident: ref26 doi: 10.1109/TCYB.2022.3151234 – ident: ref18 doi: 10.1007/3-540-45682-1_20 – ident: ref32 doi: 10.1109/CIMCA.2005.1631619 – ident: ref1 doi: 10.1038/s41467-020-16175-y – ident: ref23 doi: 10.1109/TCYB.2020.3005047 – ident: ref30 doi: 10.1109/TR.2023.3258983 – ident: ref4 doi: 10.1007/978-981-33-4543-0_57 – ident: ref15 doi: 10.1109/SFCS.1982.38 – ident: ref27 doi: 10.1007/978-3-031-36622-2_30 – ident: ref29 doi: 10.1007/3-540-48910-X_16 – start-page: 35 volume-title: Proc. ACM SIGSAC Conf. Comput. Commun. Secur. ident: ref6 article-title: ABY3: A mixed protocol framework for machine learning – ident: ref31 doi: 10.1109/SP.2017.12 – ident: ref38 doi: 10.1109/ICIST.2019.8836906 – ident: ref19 doi: 10.1023/A:1006529012972 – ident: ref3 doi: 10.1109/TEVC.2021.3080683 – ident: ref14 doi: 10.1109/TCYB.2020.2983860 – volume: 9 start-page: 193 issue: 3 year: 1995 ident: ref34 article-title: Genetic algorithms, tournament selection, and the effects of noise publication-title: Complex Syst. – ident: ref22 doi: 10.1109/TCYB.2022.3224169 – ident: ref2 doi: 10.1002/9781118600283 – ident: ref10 doi: 10.1007/978-3-540-74553-2_38 – ident: ref13 doi: 10.1145/3158363 – ident: ref11 doi: 10.1007/978-3-642-15871-1_5 – ident: ref16 doi: 10.1504/IJACT.2014.062738 – ident: ref12 doi: 10.1109/tevc.2023.3319566 – ident: ref17 doi: 10.1109/JSTSP.2015.2429117 – ident: ref33 doi: 10.7551/mitpress/1090.001.0001 – ident: ref25 doi: 10.1109/MCI.2023.3327892 – start-page: 133 volume-title: Proc. 3rd Int. Conf. Genet. Algorithms ident: ref35 article-title: Scheduling problems and Traveling salesmen: The genetic edge recombination operator – ident: ref39 doi: 10.1109/tifs.2024.3402173 – start-page: 201 volume-title: Proc. 33rd Int. Conf. Mach. Learn. ident: ref5 article-title: Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy – ident: ref37 doi: 10.1007/978-3-662-03315-9_4 – ident: ref40 doi: 10.1145/3485832.3485842 – ident: ref21 doi: 10.1109/ICACI52617.2021.9435860 – ident: ref8 doi: 10.1145/3372297.3417274 – ident: ref28 doi: 10.1109/TIFS.2022.3211707
SSID	ssj0000816898
Score	2.5198498
Snippet	Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited... EA, such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources,...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	3638
SubjectTerms	Approximation Cloud computing Combinatorial analysis Combinatorial optimization Datasets Encryption Evolutionary algorithms Evolutionary computation evolutionary computation as a service (ECaaS) Genetic algorithms Knapsack problem Optimization Privacy privacy protection secure computing Servers Social factors Statistics Traveling salesman problem
Title	PEGA: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization
URI	https://ieeexplore.ieee.org/document/10398448 https://www.ncbi.nlm.nih.gov/pubmed/38215330 https://www.proquest.com/docview/3062736353 https://www.proquest.com/docview/2929033210
Volume	54
WOSCitedRecordID	wos001174076900001&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: 2168-2275 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816898 issn: 2168-2267 databaseCode: RIE dateStart: 20130101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NT9wwEB0V1AOXtlDabkuRK_XQVvLixE4cc9siaKVKdA9U2p6ixJkAEmSr_UDi3zPjeFf0ABI3S7ETKzMev_F45gF8tsb4LG8TWSVNK8lKNrIwqCU6ZzKC-zRMBbIJe3paTCZuHJPVQy4MIobLZzjkZojlN1O_5KOyAw5bFuRPbMCGtXmfrLU-UAkMEoH7NqWGJFhhYxQzUe7g7Ojv9yFThQ-1NnmRM3-OLlJGO-q_LSlwrDwMN8O2c_LyiRN-BS8ivhSjXiG24Rl2O7AdV_BcfIllpr_uwBbjzL5M82v4NT7-MToUIzGeXd5U_lby1Qw2I9254CHUS4yuzqezy8XFtSCgK8iQkFPNLjtpsPhNluc6pnTuwp-T47OjnzLyLEivM7WQlfImMTVXeksx1bTF157sDrnNaZtnbeobU7u8bpgqXSPmrknS2rraoLMea6ffwGY37fAdCLS2qbBJdGa4SCmhH9vatvLKc2HA3AxArX516WMRcubCuCqDM6JcyYIqWVBlFNQAvq2H_OsrcDzWeZelcK9jL4AB7K0EWsZFOi81l2jWhLho2Kf1Y1peHDOpOpwu52VK8FFpTnQawNteEdYvX-nP-wc--gG2aG6mv1i2B5uL2RI_wnN_Q8Kd7ZMOT4r9oMN3s8Lndw
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB1BQaKX0pYCCy0YiQMgeevEThxz21YtRS3LHhapnKzEcUqlNlvtRyX-PTOOd1UOReJmKXZiZcbjNx7PPID3WimX5U3Cy6RuOFrJmhfKS-6NURnCfRwmAtmEHg6L83MzisnqIRfGex8un_k-NUMsv564BR2V7VPYskB_4iE8IuqsmK61OlIJHBKB_TbFBkdgoWMcMxFmf3z486BPZOF9KVVe5MSgI4uU8I74a1MKLCv3A86w8Rw__c8pb8JGRJhs0KnEFjzw7TZsxTU8Yx9ioemP27BOSLMr1PwMTkdHXwaf2YCNppe3pfvN6XIGGZL2gtEQ7MUGVxeT6eX81zVDqMvQlKBbTU476jD7jrbnOiZ17sCP46Px4QmPTAvcyUzMeSmcSlRFtd5Sn0rc5CuHlgcd57TJsyZ1tapMXtVEli69z02dpJU2lfJGO18Z-RzW2knrXwLzWtelrxOZKSpTivhHN7opnXBUGjBXPRDLX21dLENObBhXNrgjwlgSlCVB2SioHnxaDbnpanD8q_MOSeFOx04APdhdCtTGZTqzkoo0S8RcOOzd6jEuMIqalK2fLGY2RQApJKU69eBFpwirly_159U9H30LT07G387s2dfh6WtYx3mq7prZLqzNpwu_B4_dLQp6-iZo8h94uenY
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=PEGA%3A+A+Privacy-Preserving+Genetic+Algorithm+for+Combinatorial+Optimization&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Bowen%2C+Zhao&rft.au=Wei-Neng+Chen&rft.au=Feng-Feng%2C+Wei&rft.au=Liu%2C+Ximeng&rft.date=2024-06-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=2168-2267&rft.eissn=2168-2275&rft.volume=54&rft.issue=6&rft.spage=3638&rft_id=info:doi/10.1109%2FTCYB.2023.3346863&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon