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Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. 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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands that the user have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. Since genetic algorithms (GAs) work with a population of points, it seems natural to use GAs in multiobjective optimization problems to capture a number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias toward some regions. In this paper, we investigate Goldberg's notion of nondominated sorting in GAs along with a niche and speciation method to find multiple Pareto-optimal points simultaneously. The proof-of-principle results obtained on three problems used by Schaffer and others suggest that the proposed method can be extended to higher dimensional and more difficult multiobjective problems. 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to create an offspring individual is proposed. The extension is experimentally evaluated on a test suite of functions differing in their modality and separability and the regular\u002Firregular arrangement of their local optima. Multiparent diagonal crossover and uniform scanning crossover and a multiparent version of intermediary recombination are considered in the experiments. The performance of the algorithm is observed to depend on the particular combination of recombination operator and objective function. In most of the cases a significant increase in performance is observed as the number of parents increases. However, there might also be no significant impact of recombination at all, and for one of the unimodal objective functions, the performance is observed to deteriorate over the course of evolution for certain choices of the recombination operator and the number of parents. Additional experiments with a skewed initialization of the population clarify that intermediary recombination does not cause a search bias toward the origin of the coordinate system in the case of domains of variables that are symmetric around zero.\u003C\u002Fjats:p>",{"EN":395},"Empirical Investigation of Multiparent Recombination Operators in Evolution Strategies",{"VOID":397},"10021763",{"VOID":399},"10.1162\u002Fevco.1997.5.3.347",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F5\u002F3\u002F347-365\u002F798",[403,422],{"id":404,"sortIndex":25,"researcher":24,"roles":405,"affiliations":406,"properties":415,"displayName":419,"givenName":24,"familyName":24},"10bb2076-5fc4-49ba-b63a-7cb3f0f50da7",[],[407],{"id":408,"sortIndex":25,"affiliation":409,"properties":24},"19433ab9-068b-4ed9-803e-580b204006c9",{"id":408,"createTime":24,"updateTime":24,"relativeEntities":410,"slug":24,"properties":411,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":414,"statistic":24},[],{"title":412},{"EN":413},"Department of Mathematics and Computer Science Leiden University Niels Bohrweg 1 NL-2333 CA, Leiden The Netherlands gusz@wi.leidenuniv.nl#TAB#",[],{"orcid":416,"title":418,"openalex":420},{"VOID":417},"https:\u002F\u002Forcid.org\u002F0000-0002-3106-4213",{"EN":419},"A. 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Eiben",{"VOID":421},"A5074492402",{"id":423,"sortIndex":136,"researcher":24,"roles":424,"affiliations":425,"properties":434,"displayName":438,"givenName":24,"familyName":24},"4c66ef13-863a-4742-8fb4-473fc53f34bd",[],[426],{"id":427,"sortIndex":25,"affiliation":428,"properties":24},"947e4afd-a6a3-47bf-a40a-735d5d486a26",{"id":427,"createTime":24,"updateTime":24,"relativeEntities":429,"slug":24,"properties":430,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":433,"statistic":24},[],{"title":431},{"EN":432},"Informatik Centrum Dortmund Center for Applied Systems Analysis Joseph-von-Fraunhofer-Strasse 20 D-44227 Dortmund, Germany and Department of Mathematics and Computer Science Leiden University Niels Bohrweg 1 NL-2333 CA, Leiden The Netherlands",[],{"orcid":435,"title":437,"openalex":439},{"VOID":436},"https:\u002F\u002Forcid.org\u002F0000-0001-6768-1478",{"EN":438},"Thomas Bäck",{"VOID":440},"A5062646838",{"url":24,"publisher":442,"properties":480},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":443,"slug":10,"properties":444,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":449,"manageAffiliations":454,"indexDatabases":465,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":445,"eissn":446,"issn":447,"title":448},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[450],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":451,"label":452,"description":453,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[455,460],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":456,"slug":24,"properties":457,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":459,"statistic":24},[],{"title":458},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":461,"slug":24,"properties":462,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":464,"statistic":24},[],{"title":463},{"EN":46},[],[466,473],{"id":50,"indexDatabase":467,"url":61,"indexYears":62,"academicFieldIds":472,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":468,"label":469,"description":470,"key":58,"publicationTags":471,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":474,"url":80,"indexYears":24,"academicFieldIds":479,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":475,"label":476,"description":477,"key":76,"publicationTags":478,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":481,"pages":482,"volume":484},{"VOID":355},{"VOID":483},"347-365",{"VOID":485},"5",122,{"total":486,"publishYear":488,"statisticByYear":489},1997,{"2012":490,"2013":491,"2014":492,"2015":490,"2016":136,"2017":156,"2018":156,"2020":493,"2021":490,"2022":493},5,9,7,3,"1997-09-01",[65,78],[497,501,505,508,512,515,518,521,524,527,530],{"id":24,"text":498,"url":24,"identifiers":499},"Back, T. (1996). Evolutionary algorithms in theory and practice. New York: Oxford University Press.",{"doi":500},"10.1093\u002Foso\u002F9780195099713.001.0001",{"id":24,"text":502,"url":24,"identifiers":503},"Back, T., Fogel, D. & Michalewicz, Z. (Eds.) (1997). Handbook of evolutionary computation. New York: Oxford University Press.",{"doi":504},"10.1887\u002F0750308958",{"id":24,"text":506,"url":24,"identifiers":507},"Back, T, & Michalewicz, 2. (1997). Test landscapes. In T. Back, D. Fogel, & Z. Michalewicz (Eds.), Handbook of evolutionary computation (pp. B2.7: 14-B2.7:20). New York: Oxford University Press.",{},{"id":24,"text":509,"url":24,"identifiers":510},"Back, T, & Schwefel, H.P. (1993). An overview ofevolutionary algorithms for parameter optimization. Evolutionary Computation, 1 (I), 1-2 3.",{"doi":511},"10.1162\u002Fevco.1993.1.1.1",{"id":24,"text":513,"url":24,"identifiers":514},"Belew, K. & Booker, L. (Eds.) (1991). Proceedings of the Foziith Oiteirzntional Conference on Genetic :llgorithms. San Illateo, Ch: Morgan Kaufmann.",{},{"id":24,"text":516,"url":24,"identifiers":517},"Bersini, H. Dorigo, AI., Langerman, S., Seront, G. & Gambardella, L. (1996). Results of the First International Contest on Evolutionary Optimization. In Proceedings ofthe Third IEEE Conference o n Eyoliitioimiy Compritatioii (pp. 61 1-61 5 ) . Piscataway, NJ: IEEE Press.",{},{"id":24,"text":519,"url":24,"identifiers":520},"Toward LIP, Compiitntron, 3, 1",{},{"id":24,"text":522,"url":24,"identifiers":523},"Bremermann, H., Kogson, hl. & Salaff, S. (1966). Global properties of evolution processes. In H. Pattee, E. Edlsack, L. Fein, & A. Callahan, (Eds.),\\ktwnlnzrtomtita nizd use~dsimdations (pp. 3-41). Washington, DC: Spartan Rooks.",{},{"id":24,"text":525,"url":24,"identifiers":526},"Eihen, A. (1997). Multi-parent recornbination. In T. Back, D. Fogel, & Z. Michalewicz (Eds.), Haiidbook of FL'OhtiOliN1? ro7npiitntion (pp. C3.3.7: 1-C3.3.7:9). New York: Oxford University Press.",{},{"id":24,"text":528,"url":24,"identifiers":529},"E:iben, A, Kaue, P.E., 8i Kuttkay, Z. (1 994). Genetic algorithms with multi-parent recombination. In Y. Davidor, R-P. Schwefel, & R. Manner, (Eds.), Proceedingsofibe Third Conjii-ence on PnrallelProblem S0Li.i~.g-fi07\u002F1.littnre [Lecture Notes in Computer Science 8661 (pp. 78-87). Berlin: Springer-Verlag.",{},{"id":24,"text":531,"url":24,"identifiers":532},"Eiben, A, R Schippers, C. (1996). Multi-parent's niche: n-an; crossovers on NK-landscapes. In I I.LI. \\'oipt, 1%'. Eheling, I. Kechenberg, & H.P. Schwefel (Eds.), Piaceeding.softhe Feud Conference 011 f'nt-~lkel h n b h i i Sohiizgfifi.otn %twr [Lecture Notes in Computer Science 1 1411 (pp. 3 19-328). 13erlin: Springer-Verlag.",{},{"id":534,"createTime":535,"updateTime":535,"relativeEntities":536,"slug":537,"properties":538,"entityType":108,"verifyStatus":109,"verifyTime":535,"verifyNote":110,"languages":549,"translateLanguages":24,"viewCount":25,"primaryUrl":550,"fullTextUrl":24,"authors":551,"publicationType":172,"publisherRelationship":590,"citationCount":636,"citationInfo":637,"publishDate":651,"publishYear":638,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":652,"openAccess":24,"references":653,"isForceReanalyzing":257},"14409890-7a0b-4a06-8aca-7597d8a9fd1a","2024-10-09T21:58:12.590+00:00",[],"Evolutionary-Algorithms-for-Constrained-Parameter-Optimization-Problems",{"openalex":539,"mag":541,"abstract":543,"title":545,"doi":547},{"VOID":540},"W2164655924",{"VOID":542},"2164655924",{"EN":544},"\u003Cjats:p> Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. \u003C\u002Fjats:p>\u003Cjats:p> In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods. \u003C\u002Fjats:p>",{"EN":546},"Evolutionary Algorithms for Constrained Parameter Optimization Problems",{"VOID":548},"10.1162\u002Fevco.1996.4.1.1",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F4\u002F1\u002F1-32\u002F754",[552,571],{"id":553,"sortIndex":25,"researcher":24,"roles":554,"affiliations":555,"properties":564,"displayName":568,"givenName":24,"familyName":24},"99e44a21-9740-4857-8835-2c1368c97bcf",[],[556],{"id":557,"sortIndex":25,"affiliation":558,"properties":24},"4a7eefc3-a70b-4f8a-be07-7f36a9e80704",{"id":557,"createTime":24,"updateTime":24,"relativeEntities":559,"slug":24,"properties":560,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":563,"statistic":24},[],{"title":561},{"VI":562},"University of North Carolina, Charlotte",[],{"orcid":565,"title":567,"openalex":569},{"VOID":566},"https:\u002F\u002Forcid.org\u002F0000-0001-5609-5457",{"EN":568},"Zbigniew Michalewicz",{"VOID":570},"A5072022501",{"id":572,"sortIndex":136,"researcher":24,"roles":573,"affiliations":574,"properties":583,"displayName":587,"givenName":24,"familyName":24},"678ba83b-b86f-475f-bb46-794633f725fe",[],[575],{"id":576,"sortIndex":25,"affiliation":577,"properties":24},"39490069-9b58-44dd-8984-4caf2e6f3070",{"id":576,"createTime":24,"updateTime":24,"relativeEntities":578,"slug":24,"properties":579,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":582,"statistic":24},[],{"title":580},{"EN":581},"Centre de Mathématiques Appliquées - Ecole Polytechnique",[],{"orcid":584,"title":586,"openalex":588},{"VOID":585},"https:\u002F\u002Forcid.org\u002F0000-0003-1450-6830",{"EN":587},"Marc Schoenauer",{"VOID":589},"A5081938847",{"url":24,"publisher":591,"properties":629},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":592,"slug":10,"properties":593,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":598,"manageAffiliations":603,"indexDatabases":614,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":594,"eissn":595,"issn":596,"title":597},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[599],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":600,"label":601,"description":602,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[604,609],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":605,"slug":24,"properties":606,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":608,"statistic":24},[],{"title":607},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":610,"slug":24,"properties":611,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":613,"statistic":24},[],{"title":612},{"EN":46},[],[615,622],{"id":50,"indexDatabase":616,"url":61,"indexYears":62,"academicFieldIds":621,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":617,"label":618,"description":619,"key":58,"publicationTags":620,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":623,"url":80,"indexYears":24,"academicFieldIds":628,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":624,"label":625,"description":626,"key":76,"publicationTags":627,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":630,"pages":632,"volume":634},{"VOID":631},"1",{"VOID":633},"1-32",{"VOID":635},"4",1628,{"total":636,"publishYear":638,"statisticByYear":639},1996,{"2012":640,"2013":641,"2014":642,"2015":643,"2016":644,"2017":645,"2018":646,"2019":647,"2020":648,"2021":649,"2022":645,"2023":643,"2024":650},89,70,95,68,65,53,52,55,50,54,46,"1996-03-01",[65,78],[654,657,660,663,666,669,672,675,678,681,684,687,690,693,696,699,703,706,709,712,715,718,721,724],{"id":24,"text":655,"url":24,"identifiers":656},"Back, T., Hoffmeister, F. & Schwefel, H.P. (199 1). A survey of evolution strategies. In R. K. Belew & L. B. Booker (Eds.), Pmceedizgs of the Fourth lntemational Conference on Genetic Algorithms (pp. 2-9). San Mateo, CA: Morgan Kaufmann.",{},{"id":24,"text":658,"url":24,"identifiers":659},"Bean, J. C. & Hadj-Alouane, A. B. (1992). Adiialgeizeticalgoritbmforboll?zded integerprogr-ams. Technical Report T R 92-53, Ann Arbor, MI: University ofMichigan, Department of Industrial and Operations Engineering.",{},{"id":24,"text":661,"url":24,"identifiers":662},"Bilchev, G. (1995). Private communication.",{},{"id":24,"text":664,"url":24,"identifiers":665},"Bilchev, G. & Parmee, I. (1995). Ant colony search vs. genetic algorithms. Technical Report. Plymouth, UK: University of Plymouth, Plymouth Engineering Design Centre.",{},{"id":24,"text":667,"url":24,"identifiers":668},"Colorni, A., Dorigo, M. & Maniezzo, V. (1991). Distributed optimization by ant colonies. In P. Bourgine & F. Varela (Eds.), Aaceedingsof the First European Coizference o n Artificial L i f . Cambridge, MA: M I T PresdBradford Books.",{},{"id":24,"text":670,"url":24,"identifiers":671},"Davis, L. (1989). Adapting operator probabilities in genetic algorithms. In J. D. Schaffer (Ed.), Proccedivzgs of the Third Intematio2nl Conference on Genetic Algorithms (pp. 61-69). San Mateo, CA: Morgan Kaufmann.",{},{"id":24,"text":673,"url":24,"identifiers":674},"Davis, L. (1995) Private communication.",{},{"id":24,"text":676,"url":24,"identifiers":677},"De Jong, K. (1 975). The adysi.s ofthe bebanior- of a clnss ofgenetic adaptive systems. Doctoral dissertation, University of Michigan, Ann Arbor. L)i.rseitatim~ Abstl-acts htematiowal,6( lo), 5 140B. (University Microfilms No 76-9381).",{},{"id":24,"text":679,"url":24,"identifiers":680},"Eiben, A., Raue, P.E. & Ruttkay, Z. (1994). Genetic algorithms with multi-parent recombination. In Y. Davidor, H.P. Schwefel, & R. MCnner (Eds.), Proceedings ojtbe Third Conference o n Paidlel Problem Solvingfi-om Napwe, Volume 866 of Lectwe Notes in Computer Science (pp. 78-87). Berlin: Springer-Verlag.",{},{"id":24,"text":682,"url":24,"identifiers":683},"Eshelman, L. & Schaffer, J. D. (1993). Real-coded genetic algorithms and interval-schemata. In L. D. Whitley (Ed.), Foendntionr of Genetic Algovithms 2 (pp. 187-202). Los Altos, CA: Morgan Kaufmann. parameter spaces. In Paceediizgs of the Third Annual Confkreie on Euokitioizaiy Progrumviing (pp. 84-97). River Edge, NJ: World Scientific.",{},{"id":24,"text":685,"url":24,"identifiers":686},"Michalewicz, Z. & Nazhiyath, G. (1995). Genocop 111: A co-evolutionary algorithm for numerical optimization problems with nonlinear constraints. In D. B. Fogel (Ed.), PF-oceedingsof the Second IEEE bzteinntional Coizfererice o?a Eziokitionaiy Compiitation (pp. 647-65 1). Piscataway, NJ: IEEE Press.",{},{"id":24,"text":688,"url":24,"identifiers":689},"Michalewicz, Z., Nazhiyath, G. & Michalewicz, M. (1996). A note on usefulness of geometrical crossover for numerical optimization problems. In P. J. Angeline & T. Back (Eds.), Proceediizgs ofthe Ftfth A?zmal Conference on Eziohtionaiy Programming. Cambridge, MA: M I T Press. In press.",{},{"id":24,"text":691,"url":24,"identifiers":692},"Muhlenbein, H. & Voigt, HI.M. (1995). Gene pool recombination for the breeder genetic algorithm. In Proceedings of the 1nteirr.ational Couference on i21etaheiiristicsfor Optimization (pp. 19-2 5 ) . ilordrecht, The Netherlands: Kluwer Publishing.",{},{"id":24,"text":694,"url":24,"identifiers":695},"Myung, H., Kim, J.H. & Fogel, D. (1995). Preliminary investigation into a two-stage method of evolutionary optimization on constrained problems. in J. R. McDonnell, R. G. Reynolds, & D. B. Fogel (Eds.), Proceediugr of the Foirith Allllllal Coizference 011 Evoliitiomq Progr-amming (pp. 449-463). Cambridge, MA: M I T Press.",{},{"id":24,"text":697,"url":24,"identifiers":698},"Orvosh, D. & Davis, L. (1993). Shall we repair? Genetic algorithms, combinatorial optimization, and feasibility constraints. In S. Forrest (Ed.), Proceedings ofthe Fifth Intei7zntio?zal Coi$erencr on Gemtic Algorithms (p. 650). San Mateo, CA: Morgan Kaufmann.",{},{"id":24,"text":700,"url":24,"identifiers":701},"Paredis, J. ( I 994). Coevolutionary constraint satisfaction. In Y. Davidor, H.P. Schwefel, & R. Manner (Eds.), Proceedings of the Third Coi$ei.eizce on Parallel Problem SolzGngfi-om Nature (pp. 46-55). Berlin: Springer-Verlag.",{"doi":702},"10.1007\u002F3-540-58484-6_249",{"id":24,"text":704,"url":24,"identifiers":705},"Parrnee, I. & Purchase, G. (1994). The development of directed genetic search technique for heavily constrained design spaces. In Proceedings of the Collfel-ence on Adaptiue Computing ii7 Engineering Desig-17 mid Control (pp. 97-102). Plymouth, UK: University of Plymouth.",{},{"id":24,"text":707,"url":24,"identifiers":708},"Powell, D. & Skolnick, M. M. (1993). Using genetic algorithms in engineering design optimization with non-linear constraints. In S. Forrest (Ed.), Proceedings of the Fifth bzteirzntional Coifereiice 077 Genetic Algorithms (pp. 424-430). San Mateo, CA: Morgan Kaufmann.",{},{"id":24,"text":710,"url":24,"identifiers":711},"Renders, J.M. & Bersini, H. (1 994). Hybridizing genetic algorithms with hill-climbing methods for global optimization: Two possible ways. In 2. Michalewicz, J. D. Schaffer, 11.P. Schwefel, D. B. Fogel, & H . Kitano (Eds.), Pruceedi7gr of the Fiirt IEEE Inte?rzatio?znl Coifereme o n Evolritioizniy Compitutioiz (pp. 3 12-3 17). Piscataway, NJ: IFXE Press.",{},{"id":24,"text":713,"url":24,"identifiers":714},"Keynolds R., 1994, Proceedings ofthe Third Amial Coizfererzce 011 Ezioliitionaty Programming (pp., 13, 1",{},{"id":24,"text":716,"url":24,"identifiers":717},"Reynolds, R., Michalewicz, Z. & Cavaretta, M. (1995). Using cultural algorithms for constraint handling in Genocop. In J. R. McDonnell, R. G. Reynolds, & D. B. Fogel (Eds.), Proceedings ofthe Fourth Anma1 Coizfereizce on Evolutionary A-ogramming (pp. 298-305). Cambridge, MA: M I T Press.",{},{"id":24,"text":719,"url":24,"identifiers":720},"Richardson, J. T, Palmer, M. R., Liepins, G. & Hilliard, M. (1989). Some guidelines for genetic algorithms with penalty functions. In J. D. Schaffer (Ed.), Proceedingr of the Third 17ztei7zatioiml Coizference 017 Genetic Algorithms (pp. 191-197). San Mateo, CA: Morgan Kaufmann.",{},{"id":24,"text":722,"url":24,"identifiers":723},"Schaffer, D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In J. J. Grefenstette (Ed.), Proceedings of\" the First Inteirzational Corzference on Genetic Algorithms. New York: Laurence Erlbaum Associates.",{},{"id":24,"text":725,"url":24,"identifiers":726},"Schoenauer, M. & Michalewicz, Z. (1996). Evolutionary computation at the edge of feasibility. In W. Ebeling & H.M. Voigt (Eds.), Paceediizgs of the Foziith Corzfeiwzee on Parallel Problem Solvingfi-om Natzire. Berlin: Springer-Verlag.",{},{"id":728,"createTime":729,"updateTime":729,"relativeEntities":730,"slug":731,"properties":732,"entityType":108,"verifyStatus":109,"verifyTime":729,"verifyNote":110,"languages":745,"translateLanguages":24,"viewCount":25,"primaryUrl":746,"fullTextUrl":24,"authors":747,"publicationType":172,"publisherRelationship":782,"citationCount":826,"citationInfo":827,"publishDate":236,"publishYear":221,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":837,"openAccess":24,"references":838,"isForceReanalyzing":257},"5e7f1ec3-a5c6-405d-a12c-626f23173d9a","2024-10-03T23:35:07.741+00:00",[],"Multiobjective-Evolutionary-Algorithms-Analyzing-the-State-of-the-Art",{"openalex":733,"mag":735,"abstract":737,"title":739,"pm":741,"doi":743},{"VOID":734},"W2125502051",{"VOID":736},"2125502051",{"EN":738},"\u003Cjats:p> Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work. \u003C\u002Fjats:p>",{"EN":740},"Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art",{"VOID":742},"10843518",{"VOID":744},"10.1162\u002F106365600568158",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F8\u002F2\u002F125-147\u002F870",[748,765],{"id":749,"sortIndex":25,"researcher":24,"roles":750,"affiliations":751,"properties":760,"displayName":762,"givenName":24,"familyName":24},"8d2fce12-1bef-4aaf-b4b5-0b9a688d4851",[],[752],{"id":753,"sortIndex":25,"affiliation":754,"properties":24},"8296a374-ac65-471c-9bc8-3751993d7c36",{"id":753,"createTime":24,"updateTime":24,"relativeEntities":755,"slug":24,"properties":756,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":759,"statistic":24},[],{"title":757},{"EN":758},"Air Force Research Laboratory, Optical Radiation Branch, Brooks AFB, TX 78235, USA",[],{"title":761,"openalex":763},{"EN":762},"David A. Van Veldhuizen",{"VOID":764},"A5030786672",{"id":766,"sortIndex":136,"researcher":24,"roles":767,"affiliations":768,"properties":777,"displayName":779,"givenName":24,"familyName":24},"80f760fd-0796-4bdf-b048-fd93ce6d8305",[],[769],{"id":770,"sortIndex":25,"affiliation":771,"properties":24},"3090461e-c9ad-40c7-99b5-b5ced63551fd",{"id":770,"createTime":24,"updateTime":24,"relativeEntities":772,"slug":24,"properties":773,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":776,"statistic":24},[],{"title":774},{"VI":775},"Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433 USA",[],{"title":778,"openalex":780},{"EN":779},"Gary B. Lamont",{"VOID":781},"A5014121562",{"url":24,"publisher":783,"properties":821},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":784,"slug":10,"properties":785,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":790,"manageAffiliations":795,"indexDatabases":806,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":786,"eissn":787,"issn":788,"title":789},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[791],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":792,"label":793,"description":794,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[796,801],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":797,"slug":24,"properties":798,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":800,"statistic":24},[],{"title":799},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":802,"slug":24,"properties":803,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":805,"statistic":24},[],{"title":804},{"EN":46},[],[807,814],{"id":50,"indexDatabase":808,"url":61,"indexYears":62,"academicFieldIds":813,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":809,"label":810,"description":811,"key":58,"publicationTags":812,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":815,"url":80,"indexYears":24,"academicFieldIds":820,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":816,"label":817,"description":818,"key":76,"publicationTags":819,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":822,"pages":823,"volume":825},{"VOID":214},{"VOID":824},"125-147",{"VOID":218},1175,{"total":826,"publishYear":221,"statisticByYear":828},{"2012":647,"2013":829,"2014":646,"2015":830,"2016":649,"2017":646,"2018":831,"2019":831,"2020":832,"2021":833,"2022":834,"2023":835,"2024":836},45,64,40,38,57,32,30,10,[65,78],[839,842,845,848,850,852,855,857,860,863],{"id":24,"text":840,"url":24,"identifiers":841},"10.1002\u002F(SICI)1520-6750(199702)44:1\u003C47::AID-NAV3>3.0.CO;2-M",{"doi":840},{"id":24,"text":843,"url":24,"identifiers":844},"10.1007\u002FBF03325101",{"doi":843},{"id":24,"text":846,"url":24,"identifiers":847},"10.1080\u002F03052150008941301",{"doi":846},{"id":24,"text":240,"url":24,"identifiers":849},{"doi":240},{"id":24,"text":243,"url":24,"identifiers":851},{"doi":243},{"id":24,"text":853,"url":24,"identifiers":854},"10.1109\u002F3468.650319",{"doi":853},{"id":24,"text":252,"url":24,"identifiers":856},{"doi":252},{"id":24,"text":858,"url":24,"identifiers":859},"10.1016\u002F0003-2670(92)85027-4",{"doi":858},{"id":24,"text":861,"url":24,"identifiers":862},"10.1109\u002F4235.585893",{"doi":861},{"id":24,"text":255,"url":24,"identifiers":864},{"doi":255},{"id":866,"createTime":867,"updateTime":867,"relativeEntities":868,"slug":869,"properties":870,"entityType":108,"verifyStatus":109,"verifyTime":880,"verifyNote":110,"languages":881,"translateLanguages":24,"viewCount":25,"primaryUrl":882,"fullTextUrl":24,"authors":883,"publicationType":172,"publisherRelationship":922,"citationCount":966,"citationInfo":967,"publishDate":978,"publishYear":968,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":979,"openAccess":24,"references":980,"isForceReanalyzing":257},"b7e0a3d0-f26a-4753-8c86-fe6ae3f3a840","2024-10-03T23:35:03.606+00:00",[],"An-Overview-of-Evolutionary-Algorithms-in-Multiobjective-Optimization",{"openalex":871,"mag":873,"abstract":875,"title":877,"doi":879},{"VOID":872},"W2121365620",{"VOID":874},"2121365620",{"EN":876},"\u003Cjats:p> The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performance measures with the inherently scalar way in which EAs reward individual performance, that is, number of offspring. \u003C\u002Fjats:p>\u003Cjats:p> In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality. The sensitivity of different methods to objective scaling and\u002For possible concavities in the trade-off surface is considered, and related to the (static) fitness landscapes such methods induce on the search space. From the discussion, directions for future research in multiobjective fitness assignment and search strategies are identified, including the incorporation of decision making in the selection procedure, fitness sharing, and adaptive representations. \u003C\u002Fjats:p>",{"EN":878},"An Overview of Evolutionary Algorithms in Multiobjective Optimization",{"VOID":243},"2024-10-03T23:35:03.605+00:00",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F3\u002F1\u002F1-16\u002F733",[884,903],{"id":885,"sortIndex":25,"researcher":24,"roles":886,"affiliations":887,"properties":896,"displayName":900,"givenName":24,"familyName":24},"5476bcb0-dd77-424b-b933-2d3337cd33ce",[],[888],{"id":889,"sortIndex":25,"affiliation":890,"properties":24},"f2a5301c-db4e-419b-a4c7-8eb8ea9d00de",{"id":889,"createTime":24,"updateTime":24,"relativeEntities":891,"slug":24,"properties":892,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":895,"statistic":24},[],{"title":893},{"EN":894},"[Department of Automatic Control and Systems Engmeering The University of Sheffield Sheffield S1 3JD, U.K. C.Fonseca@shef.ac.uk]",[],{"orcid":897,"title":899,"openalex":901},{"VOID":898},"https:\u002F\u002Forcid.org\u002F0000-0001-5162-2457",{"EN":900},"Carlos M. Fonseca",{"VOID":902},"A5038504776",{"id":904,"sortIndex":136,"researcher":24,"roles":905,"affiliations":906,"properties":915,"displayName":919,"givenName":24,"familyName":24},"1eb60403-b4df-4f63-9094-a48d9ecc7469",[],[907],{"id":908,"sortIndex":25,"affiliation":909,"properties":24},"738e40fb-6552-4433-afaa-4bf6fe343b27",{"id":908,"createTime":24,"updateTime":24,"relativeEntities":910,"slug":24,"properties":911,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":914,"statistic":24},[],{"title":912},{"EN":913},"[Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield S1 3JD, U.K. P.Fleming@shef.ac.uk]",[],{"orcid":916,"title":918,"openalex":920},{"VOID":917},"https:\u002F\u002Forcid.org\u002F0000-0001-9837-8404",{"EN":919},"P.J. Fleming",{"VOID":921},"A5071374930",{"url":24,"publisher":923,"properties":961},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":924,"slug":10,"properties":925,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":930,"manageAffiliations":935,"indexDatabases":946,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":926,"eissn":927,"issn":928,"title":929},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[931],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":932,"label":933,"description":934,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[936,941],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":937,"slug":24,"properties":938,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":940,"statistic":24},[],{"title":939},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":942,"slug":24,"properties":943,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":945,"statistic":24},[],{"title":944},{"EN":46},[],[947,954],{"id":50,"indexDatabase":948,"url":61,"indexYears":62,"academicFieldIds":953,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":949,"label":950,"description":951,"key":58,"publicationTags":952,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":955,"url":80,"indexYears":24,"academicFieldIds":960,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":956,"label":957,"description":958,"key":76,"publicationTags":959,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":962,"pages":963,"volume":965},{"VOID":631},{"VOID":964},"1-16",{"VOID":355},2242,{"total":966,"publishYear":968,"statisticByYear":969},1995,{"2012":970,"2013":971,"2014":972,"2015":973,"2016":974,"2017":644,"2018":641,"2019":833,"2020":647,"2021":975,"2022":976,"2023":829,"2024":977},105,81,116,104,77,62,42,27,"1995-03-01",[78],[981,984,986],{"id":24,"text":982,"url":24,"identifiers":983},"10.1162\u002Fevco.1993.1.3.213",{"doi":982},{"id":24,"text":249,"url":24,"identifiers":985},{"doi":249},{"id":24,"text":252,"url":24,"identifiers":987},{"doi":252},{"id":989,"createTime":990,"updateTime":990,"relativeEntities":991,"slug":992,"properties":993,"entityType":108,"verifyStatus":109,"verifyTime":990,"verifyNote":110,"languages":1006,"translateLanguages":24,"viewCount":25,"primaryUrl":1007,"fullTextUrl":24,"authors":1008,"publicationType":172,"publisherRelationship":1047,"citationCount":1092,"citationInfo":1093,"publishDate":1097,"publishYear":1094,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":1098,"openAccess":24,"references":1099,"isForceReanalyzing":257},"5809cd64-d593-43f2-9085-619db81989ed","2024-10-02T14:28:30.815+00:00",[],"A-Hidden-Markov-Model-Approach-to-the-Problem-of-Heuristic-Selection-in-Hyper-Heuristics-with-a-Case-Study-in-High-School-Timetabling-Problems",{"openalex":994,"mag":996,"abstract":998,"title":1000,"pm":1002,"doi":1004},{"VOID":995},"W2462755584",{"VOID":997},"2462755584",{"EN":999},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\n               \u003Cjats:p>Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.\u003C\u002Fjats:p>",{"EN":1001},"A Hidden Markov Model Approach to the Problem of Heuristic Selection in\n          Hyper-Heuristics with a Case Study in High School Timetabling Problems",{"VOID":1003},"27258841",{"VOID":1005},"10.1162\u002Fevco_a_00186",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F25\u002F3\u002F473\u002F1044\u002FA-Hidden-Markov-Model-Approach-to-the-Problem-of",[1009,1028],{"id":1010,"sortIndex":25,"researcher":24,"roles":1011,"affiliations":1012,"properties":1021,"displayName":1025,"givenName":24,"familyName":24},"e770c6cc-309e-42e8-a2d3-bcdf2ad04f54",[],[1013],{"id":1014,"sortIndex":25,"affiliation":1015,"properties":24},"22d75cf2-fee4-4252-89ca-8bbcd78b9a91",{"id":1014,"createTime":24,"updateTime":24,"relativeEntities":1016,"slug":24,"properties":1017,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1020,"statistic":24},[],{"title":1018},{"EN":1019},"University of Exeter, College of Engineering, Mathematics and Physical Sciences, Streatham Campus, Harrison Building, Exeter EX4 4QF, United Kingdom a.kheiri@exeter.ac.uk",[],{"orcid":1022,"title":1024,"openalex":1026},{"VOID":1023},"https:\u002F\u002Forcid.org\u002F0000-0002-6716-2130",{"EN":1025},"Ahmed Kheiri",{"VOID":1027},"A5017573824",{"id":1029,"sortIndex":136,"researcher":24,"roles":1030,"affiliations":1031,"properties":1040,"displayName":1044,"givenName":24,"familyName":24},"7a9cdfa5-101d-411f-910e-799ba21b3144",[],[1032],{"id":1033,"sortIndex":25,"affiliation":1034,"properties":24},"f2c82fae-d70d-4294-8335-7f5a5cc6bb66",{"id":1033,"createTime":24,"updateTime":24,"relativeEntities":1035,"slug":24,"properties":1036,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1039,"statistic":24},[],{"title":1037},{"EN":1038},"University of Exeter, College of Engineering, Mathematics and Physical Sciences, Streatham Campus, Harrison Building, Exeter EX4 4QF, United Kingdom e.c.keedwell@exeter.ac.uk",[],{"orcid":1041,"title":1043,"openalex":1045},{"VOID":1042},"https:\u002F\u002Forcid.org\u002F0000-0003-3650-6487",{"EN":1044},"Edward Keedwell",{"VOID":1046},"A5058871746",{"url":24,"publisher":1048,"properties":1086},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1049,"slug":10,"properties":1050,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":1055,"manageAffiliations":1060,"indexDatabases":1071,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":1051,"eissn":1052,"issn":1053,"title":1054},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[1056],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":1057,"label":1058,"description":1059,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[1061,1066],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":1062,"slug":24,"properties":1063,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1065,"statistic":24},[],{"title":1064},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":1067,"slug":24,"properties":1068,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1070,"statistic":24},[],{"title":1069},{"EN":46},[],[1072,1079],{"id":50,"indexDatabase":1073,"url":61,"indexYears":62,"academicFieldIds":1078,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":1074,"label":1075,"description":1076,"key":58,"publicationTags":1077,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":1080,"url":80,"indexYears":24,"academicFieldIds":1085,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":1081,"label":1082,"description":1083,"key":76,"publicationTags":1084,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":1087,"pages":1088,"volume":1090},{"VOID":355},{"VOID":1089},"473-501",{"VOID":1091},"25",37,{"total":1092,"publishYear":1094,"statisticByYear":1095},2017,{"2018":490,"2019":1096,"2020":491,"2021":1096,"2022":492,"2023":156,"2024":156},6,"2017-09-01",[65,78],[1100,1104,1107,1111,1115,1119,1123,1126,1130,1134,1138,1142,1145,1149,1153,1156,1160,1164,1168,1172,1175,1178,1182,1186,1189,1192,1196,1200,1203,1206,1210,1214,1218,1222,1225,1229,1233,1237,1240,1244,1248,1252,1256,1260,1264,1268,1272,1276,1279,1283,1287,1291,1294,1297,1301],{"id":24,"text":1101,"url":24,"identifiers":1102},"Abramson, D.\n           (1991). Constructing school timetables using\n            simulated annealing: Sequential and parallel algorithms.\n            Management Science,\n            37(1):98–113.",{"doi":1103},"10.1287\u002Fmnsc.37.1.98",{"id":24,"text":1105,"url":24,"identifiers":1106},"Abramson, D. A.,\n                Dang, H.,\n            and Krisnamoorthy,\n              M. (1999).\n            Simulated annealing cooling schedules for the school timetabling\n            problem. Asia-Pacific Journal of Operational Research,\n            16(1):1–22.",{},{"id":24,"text":1108,"url":24,"identifiers":1109},"Ahmed, L. N.,\n                Özcan, E.,\n            and Kheiri,\n            A. (2015).\n            Solving high school timetabling problems worldwide using selection\n            hyper-heuristics. Expert Systems with Applications,\n            42(13):5463–5471.",{"doi":1110},"10.1016\u002Fj.eswa.2015.02.059",{"id":24,"text":1112,"url":24,"identifiers":1113},"Alvarez-Valdés, R.,\n                Parreño,\n            F., and\n              Tamarit, J.\n              M. (2002). A\n            tabu search algorithm for assigning teachers to courses.\n            TOP,\n            10(2):239–259.",{"doi":1114},"10.1007\u002FBF02579018",{"id":24,"text":1116,"url":24,"identifiers":1117},"Baum, L. E., and\n                Petrie,\n            T. (1966).\n            Statistical inference for probabilistic functions of finite state Markov\n            chains. The Annals of Mathematical Statistics,\n            37(6):1554–1563.",{"doi":1118},"10.1214\u002Faoms\u002F1177699147",{"id":24,"text":1120,"url":24,"identifiers":1121},"Beligiannis, G. N.,\n                Moschopoulos, C.\n              N., Kaperonis,\n                G. P., and\n                Likothanassis, S.\n              D. (2008).\n            Applying evolutionary computation to the school timetabling problem: The\n            Greek case. Computers and Operations Research,\n            35(4):1265–1280.",{"doi":1122},"10.1016\u002Fj.cor.2006.08.010",{"id":24,"text":1124,"url":24,"identifiers":1125},"Bello, G. S.,\n                Rangel, M.\n              C., and Boeres,\n                M. C. S.\n          (2008). An approach for the class\u002Fteacher timetabling\n            problem. In Proceedings of the 7th International Conference on\n            the Practice and Theory of Automated Timetabling, pp.\n            1–6.",{},{"id":24,"text":1127,"url":24,"identifiers":1128},"Bilgin, B.,\n                Özcan, E.,\n            and Korkmaz, E.\n              E. (2007). An\n            experimental study on hyper-heuristics and exam scheduling. In\n            E.\n                K.Burke and\n                H.Rudová\n          (Eds.), Practice and Theory of Automated Timetabling VI, volume\n            3867 of Lecture Notes in Computer Science, pp.\n            394–412.",{"doi":1129},"10.1007\u002F978-3-540-77345-0_25",{"id":24,"text":1131,"url":24,"identifiers":1132},"Birbas, T.,\n                Daskalaki,\n              S., and\n                Housos,\n            E. (2009).\n            School timetabling for quality student and teacher\n            schedules. Journal of Scheduling,\n            12(2):177–197.",{"doi":1133},"10.1007\u002Fs10951-008-0088-2",{"id":24,"text":1135,"url":24,"identifiers":1136},"Bufé, M.,\n                Fischer,\n            T., Gubbels,\n                H., Häcker,\n                C.,\n                Hasprich,\n              O., Scheibel,\n                C.,\n              Weicker, K.,\n                Weicker,\n            N., Wenig,\n                M., and\n                Wolfangel,\n              C. (2001).\n            Automated solution of a highly constrained school timetabling\n            problem—Preliminary results. In E. J.\n              W.Boers (Ed.),\n            Applications of Evolutionary Computing, volume 2037 of\n            Lecture Notes in Computer Science, pp.\n            431–440.",{"doi":1137},"10.1007\u002F3-540-45365-2_45",{"id":24,"text":1139,"url":24,"identifiers":1140},"Burke, E. 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(2002).\n            A hybrid genetic algorithm for school timetabling. In\n            B.McKay\n            and\n            J.Slaney\n          (Eds.), AI 2002: Advances in Artificial Intelligence, volume\n            2557 of Lecture Notes in Computer Science, pp.\n            455–464.",{"doi":1290},"10.1007\u002F3-540-36187-1_40",{"id":24,"text":1292,"url":24,"identifiers":1293},"Wilke, P., and\n                Killer,\n            H. (2010).\n            Walk down jump up algorithm: A new hybrid algorithm for timetabling\n            problems. In Proceedings of the 8th International Conference on\n            the Practice and Theory of Automated Timetabling, pp.\n            440–446.",{},{"id":24,"text":1295,"url":24,"identifiers":1296},"Wolpert, D. H., and\n                Macready, W.\n              G. (1997). No\n            free lunch theorems for optimization. IEEE Transactions on\n            Evolutionary Computation,\n          1:67–82.",{"doi":861},{"id":24,"text":1298,"url":24,"identifiers":1299},"Wright, M. B.\n           (1996). School timetabling using heuristic\n            search. Journal of the Operational Research Society,\n            47(3):347–357.",{"doi":1300},"10.1057\u002Fjors.1996.34",{"id":24,"text":1302,"url":24,"identifiers":1303},"Zhang, D.,\n                Liu, Y.,\n                M’Hallah,\n              R., and\n              Leung, S. C.\n              H. (2010). A\n            simulated annealing with a new neighborhood structure based algorithm for high school\n            timetabling problems. European Journal of Operational\n            Research,\n            203(3):550–558.",{"doi":1304},"10.1016\u002Fj.ejor.2009.09.014",{"id":1306,"createTime":1307,"updateTime":1307,"relativeEntities":1308,"slug":1309,"properties":1310,"entityType":108,"verifyStatus":109,"verifyTime":1307,"verifyNote":110,"languages":1321,"translateLanguages":24,"viewCount":25,"primaryUrl":1322,"fullTextUrl":24,"authors":1323,"publicationType":172,"publisherRelationship":1341,"citationCount":1385,"citationInfo":1386,"publishDate":1397,"publishYear":968,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":1398,"openAccess":24,"references":1399,"isForceReanalyzing":257},"2da73c64-870e-4506-bd3e-f160742915b9","2024-09-28T08:10:20.117+00:00",[],"Strongly-Typed-Genetic-Programming",{"openalex":1311,"mag":1313,"abstract":1315,"title":1317,"doi":1319},{"VOID":1312},"W2027649639",{"VOID":1314},"2027649639",{"EN":1316},"\u003Cjats:p> Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection (Koza, 1992). However, in its standard form, there is no way to restrict the programs it generates to those where the functions operate on appropriate data types. In the case when the programs manipulate multiple data types and contain functions designed to operate on particular data types, this can lead to unnecessarily large search times and\u002For unnecessarily poor generalization performance. Strongly typed genetic programming (STGP) is an enhanced version of genetic programming that enforces data-type constraints and whose use of generic functions and generic data types makes it more powerful than other approaches to type-constraint enforcement. After describing its operation, we illustrate its use on problems in two domains, matrix\u002Fvector manipulation and list manipulation, which require its generality. The examples are (1) the multidimensional least-squares regression problem, (2) the multidimensional Kalman filter, (3) the list manipulation function NTH, and (4) the list manipulation function MAPCAR. \u003C\u002Fjats:p>",{"EN":1318},"Strongly Typed Genetic Programming",{"VOID":1320},"10.1162\u002Fevco.1995.3.2.199",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F3\u002F2\u002F199-230\u002F731",[1324],{"id":1325,"sortIndex":25,"researcher":24,"roles":1326,"affiliations":1327,"properties":1336,"displayName":1338,"givenName":24,"familyName":24},"e4952b44-c6dd-48bf-a831-b4330ec91b71",[],[1328],{"id":1329,"sortIndex":25,"affiliation":1330,"properties":24},"2f272929-802e-4847-a518-71324232c4e8",{"id":1329,"createTime":24,"updateTime":24,"relativeEntities":1331,"slug":24,"properties":1332,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1335,"statistic":24},[],{"title":1333},{"EN":1334},"Bolt Beranek and Newman, Inc. 70 Fawcett Street Cambridge, MA 02138 dmontana@bbn.com#TAB#",[],{"title":1337,"openalex":1339},{"EN":1338},"David J. Montana",{"VOID":1340},"A5103330610",{"url":24,"publisher":1342,"properties":1380},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1343,"slug":10,"properties":1344,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":1349,"manageAffiliations":1354,"indexDatabases":1365,"url":83,"thumbnailPath":24,"statistic":24,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":1345,"eissn":1346,"issn":1347,"title":1348},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[1350],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":1351,"label":1352,"description":1353,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},[1355,1360],{"id":35,"createTime":24,"updateTime":24,"relativeEntities":1356,"slug":24,"properties":1357,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1359,"statistic":24},[],{"title":1358},{"EN":39},[],{"id":42,"createTime":24,"updateTime":24,"relativeEntities":1361,"slug":24,"properties":1362,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1364,"statistic":24},[],{"title":1363},{"EN":46},[],[1366,1373],{"id":50,"indexDatabase":1367,"url":61,"indexYears":62,"academicFieldIds":1372,"indexDatabaseRanking":65},{"id":52,"createTime":24,"updateTime":24,"relativeEntities":1368,"label":1369,"description":1370,"key":58,"publicationTags":1371,"standard":24},[],{"EN":55,"VI":55},{"EN":55,"VI":57},[60],[64],{"id":67,"indexDatabase":1374,"url":80,"indexYears":24,"academicFieldIds":1379,"indexDatabaseRanking":24},{"id":69,"createTime":24,"updateTime":24,"relativeEntities":1375,"label":1376,"description":1377,"key":76,"publicationTags":1378,"standard":24},[],{"EN":72,"VI":72},{"EN":74,"VI":75},[78,79],[82],{"issue":1381,"pages":1382,"volume":1384},{"VOID":214},{"VOID":1383},"199-230",{"VOID":355},877,{"total":1385,"publishYear":968,"statisticByYear":1387},{"2012":1388,"2013":1092,"2014":1389,"2015":1390,"2016":1391,"2017":1392,"2018":1393,"2019":1394,"2020":650,"2021":1395,"2022":1394,"2023":1392,"2024":1396},34,25,28,18,20,23,24,85,17,"1995-06-01",[78],[1400],{"id":24,"text":1401,"url":24,"identifiers":1402},"10.1115\u002F1.3662552",{"doi":1401},{"id":1404,"createTime":1405,"updateTime":1405,"relativeEntities":1406,"slug":1407,"properties":1408,"entityType":108,"verifyStatus":109,"verifyTime":1405,"verifyNote":110,"languages":1421,"translateLanguages":24,"viewCount":25,"primaryUrl":1422,"fullTextUrl":24,"authors":1423,"publicationType":172,"publisherRelationship":1459,"citationCount":1504,"citationInfo":1505,"publishDate":1513,"publishYear":1506,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":1514,"openAccess":24,"references":1515,"isForceReanalyzing":257},"8485d0d1-2c6a-47a7-bb68-2bb82ec8126b","2024-09-12T17:09:02.860+00:00",[],"Introducing-Robustness-in-Multi-Objective-Optimization",{"openalex":1409,"mag":1411,"abstract":1413,"title":1415,"pm":1417,"doi":1419},{"VOID":1410},"W2166364496",{"VOID":1412},"2166364496",{"EN":1414},"\u003Cjats:p> In optimization studies including multi-objective optimization, the main focus is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. However, in practice, users may not always be interested in finding the so-called global best solutions, particularly when these solutions are quite sensitive to the variable perturbations which cannot be avoided in practice. In such cases, practitioners are interested in finding the robust solutions which are less sensitive to small perturbations in variables. Although robust optimization is dealt with in detail in single-objective evolutionary optimization studies, in this paper, we present two different robust multi-objective optimization procedures, where the emphasis is to find a robust frontier, instead of the global Pareto-optimal frontier in a problem. The first procedure is a straightforward extension of a technique used for single-objective optimization and the second procedure is a more practical approach enabling a user to set the extent of robustness desired in a problem. To demonstrate the differences between global and robust multi-objective optimization principles and the differences between the two robust optimization procedures suggested here, we develop a number of constrained and unconstrained test problems having two and three objectives and show simulation results using an evolutionary multi-objective optimization (EMO) algorithm. Finally, we also apply both robust optimization methodologies to an engineering design problem. \u003C\u002Fjats:p>",{"EN":1416},"Introducing Robustness in Multi-Objective Optimization",{"VOID":1418},"17109607",{"VOID":1420},"10.1162\u002Fevco.2006.14.4.463",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F14\u002F4\u002F463-494\u002F1251",[1424,1440],{"id":1425,"sortIndex":25,"researcher":24,"roles":1426,"affiliations":1427,"properties":1436,"displayName":151,"givenName":24,"familyName":24},"c428e1fb-6617-45c8-9702-f3a1c2bc5863",[],[1428],{"id":1429,"sortIndex":25,"affiliation":1430,"properties":24},"52e03126-2be4-4eaa-96dc-0e515b708602",{"id":1429,"createTime":24,"updateTime":24,"relativeEntities":1431,"slug":24,"properties":1432,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1435,"statistic":24},[],{"title":1433},{"EN":1434},"Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, Kanpur, PIN 208016, INDIA",[],{"orcid":1437,"title":1438,"openalex":1439},{"VOID":149},{"EN":151},{"VOID":153},{"id":1441,"sortIndex":136,"researcher":24,"roles":1442,"affiliations":1443,"properties":1452,"displayName":1456,"givenName":24,"familyName":24},"a3e140cd-8fbd-432d-9a40-3858f94ef21b",[],[1444],{"id":1445,"sortIndex":25,"affiliation":1446,"properties":24},"cd74bdf7-d8cb-4966-8e54-fa48bb0baf8b",{"id":1445,"createTime":24,"updateTime":24,"relativeEntities":1447,"slug":24,"properties":1448,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1451,"statistic":24},[],{"title":1449},{"EN":1450},"IBM India Research Lab, Block 1, Indian Institute of Technology, Hauz Khas, New Delhi, PIN 110016, India",[],{"orcid":1453,"title":1455,"openalex":1457},{"VOID":1454},"https:\u002F\u002Forcid.org\u002F0000-0003-4799-5693",{"EN":1456},"Himanshu 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recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.\u003C\u002Fjats:p>",{"EN":1535},"Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms",{"VOID":1537},"31464527",{"VOID":1539},"10.1162\u002Fevco_a_00263",[112],"https:\u002F\u002Fdirect.mit.edu\u002Fevco\u002Farticle\u002F28\u002F2\u002F195-226\u002F94985",[1543,1562,1581],{"id":1544,"sortIndex":25,"researcher":24,"roles":1545,"affiliations":1546,"properties":1555,"displayName":1559,"givenName":24,"familyName":24},"643e83cc-cafd-4f0b-bfd4-2ebf738085f4",[],[1547],{"id":1548,"sortIndex":25,"affiliation":1549,"properties":24},"4dd774b1-b189-4cfa-9199-41931d94afea",{"id":1548,"createTime":24,"updateTime":24,"relativeEntities":1550,"slug":24,"properties":1551,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1554,"statistic":24},[],{"title":1552},{"EN":1553},"Instituto Metrópole Digital (IMD), Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil",[],{"orcid":1556,"title":1558,"openalex":1560},{"VOID":1557},"https:\u002F\u002Forcid.org\u002F0000-0003-4654-2553",{"EN":1559},"Leonardo C. 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