Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance coun...
Saved in:
| Published in | IEEE transactions on evolutionary computation Vol. 7; no. 5; pp. 503 - 515 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.10.2003
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1089-778X 1941-0026 |
| DOI | 10.1109/TEVC.2003.817234 |
Cover
| Abstract | The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GMN/sup 2/), where G is the number of generations, M is the number of objectives, and N is the population size. The N/sup 2/ factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN log/sup M-1/N), much faster than the O(GMN/sup 2/) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN/sup 2/) algorithms. |
|---|---|
| AbstractList | The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GMN/sup 2/), where G is the number of generations, M is the number of objectives, and N is the population size. The N/sup 2/ factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN log/sup M-1/N), much faster than the O(GMN/sup 2/) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN/sup 2/) algorithms. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN2) algorithms. The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GMN(2)), where G is the number of generations, M is the number of objectives, and N is the population size. The N(2) factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN log/M-1/N), much faster than the O(GMN(2)) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN(2)) algorithms. The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms have been published. The majority of these algorithms use fitness assignment based on Pareto-domination: Nondominated sorting, dominance counting, or identification of the nondominated solutions. The success of these algorithms indicates that this type of fitness is suitable for multiobjective problems, but so far the use of Pareto-based fitness has lead to program run times in O(GMN super(2)), where G is the number of generations, M is the number of objectives, and N is the population size. The N super(2) factor should be reduced if possible, since it leads to long processing times for large population sizes. This paper presents a new and efficient algorithm for nondominated sorting, which can speed up the processing time of some multiobjective evolutionary algorithms (MOEAs) substantially. The new algorithm is incorporated into the nondominated sorting genetic algorithm II (NSGA-II) and reduces the overall run-time complexity of this algorithm to O(GN log super(M-1)N), much faster than the O(GMN super(2)) complexity published by Deb et al. (2002). Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved significantly in terms of running time by using efficient algorithms known from computer science instead of inefficient O(MN super(2)) algorithms. |
| Author | Jensen, M.T. |
| Author_xml | – sequence: 1 givenname: M.T. surname: Jensen fullname: Jensen, M.T. organization: EVALife Group, Univ. of Aarhus, Denmark |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15200657$$DView record in Pascal Francis |
| BookMark | eNp9kU1rGzEQQJeSQpO090IvotDmtK6-Vh-9GeOmhtBC65bcFlkeJTK7kiNpS_PvI-NAIIecNIf3ZhDvrDkJMUDTvCd4RgjWX9bLv4sZxZjNFJGU8VfNKdGctBhTcVJnrHQrpbp-05zlvMOY8I7o0-b6F2wn68MNKreA0hTa4kdANo77Af77co-iQ-M0FB83O7DF_wO0nOevaF3xH78v5-1qhUzYolj9hMxwE5Mvt2N-27x2Zsjw7vE9b_58W64X39urn5erxfyqtZzL0lKGFePSbhzvGDZUKsM7Ca4TGCg4aR2A4oJuhbTKCeW43egt3nRghNbasvPm4rh3n-LdBLn0o88WhsEEiFPuNSZCSUVpJT-_SFLVyY4oVsGPz8BdnFKov-iV4lRjRnCFPj1CJlszuGSC9bnfJz-adN-TrrYQnawcPnI2xZwTuCcE94dy_aFcfyjXH8tVRTxTrC-mFgglGT-8JH44ih4Anu5QJokQ7AGAGKYO |
| CODEN | ITEVF5 |
| CitedBy_id | crossref_primary_10_1109_TNNLS_2015_2466686 crossref_primary_10_1016_j_apm_2014_06_005 crossref_primary_10_1016_j_ins_2010_09_007 crossref_primary_10_1016_j_eswa_2021_114737 crossref_primary_10_1016_j_enconman_2009_11_022 crossref_primary_10_1016_j_simpat_2024_102927 crossref_primary_10_1109_JIOT_2022_3170482 crossref_primary_10_1017_aer_2021_19 crossref_primary_10_1002_nme_2909 crossref_primary_10_1016_j_asoc_2022_109293 crossref_primary_10_1016_j_jocs_2017_09_015 crossref_primary_10_1142_S0218213016500214 crossref_primary_10_1016_j_swevo_2018_08_011 crossref_primary_10_1109_JESTPE_2023_3348206 crossref_primary_10_1016_j_asoc_2017_06_038 crossref_primary_10_1109_TEVC_2014_2366498 crossref_primary_10_1080_0305215X_2025_2460509 crossref_primary_10_1155_2017_5342727 crossref_primary_10_1080_15502287_2021_1916696 crossref_primary_10_1016_j_ins_2019_07_011 crossref_primary_10_1016_j_ejor_2009_11_003 crossref_primary_10_1093_bioinformatics_btq439 crossref_primary_10_1007_s00521_021_06459_9 crossref_primary_10_1162_evco_a_00204 crossref_primary_10_1007_s00778_024_00835_2 crossref_primary_10_1016_j_neucom_2019_08_104 crossref_primary_10_1080_00207543_2011_613866 crossref_primary_10_1007_s10852_005_2582_2 crossref_primary_10_1016_j_asoc_2025_112989 crossref_primary_10_3390_electronics10040494 crossref_primary_10_1186_s43093_024_00397_3 crossref_primary_10_1109_ACCESS_2021_3065384 crossref_primary_10_3934_mbe_2023117 crossref_primary_10_1016_j_aeue_2017_03_017 crossref_primary_10_1016_j_asoc_2012_07_023 crossref_primary_10_1049_iet_gtd_2019_0679 crossref_primary_10_1016_j_asoc_2016_11_010 crossref_primary_10_1137_17M1121184 crossref_primary_10_1109_TEVC_2010_2041060 crossref_primary_10_1016_j_eswa_2017_09_050 crossref_primary_10_1371_journal_pone_0285601 crossref_primary_10_1016_j_eswa_2019_05_032 crossref_primary_10_1007_s10489_014_0619_9 crossref_primary_10_1016_j_ijepes_2014_02_017 crossref_primary_10_4018_jaec_2010040104 crossref_primary_10_1016_j_epsr_2014_09_010 crossref_primary_10_1016_j_ijepes_2012_06_034 crossref_primary_10_1038_s41524_024_01294_7 crossref_primary_10_1109_TEVC_2018_2866927 crossref_primary_10_3390_su12218971 crossref_primary_10_1080_00207543_2013_853890 crossref_primary_10_1109_TEVC_2023_3269348 crossref_primary_10_1109_TPWRD_2007_905412 crossref_primary_10_1016_j_isatra_2012_09_004 crossref_primary_10_1016_j_scico_2021_102643 crossref_primary_10_1002_stc_2454 crossref_primary_10_1109_ACCESS_2021_3086559 crossref_primary_10_1109_TEVC_2018_2799684 crossref_primary_10_1109_TCYB_2016_2621008 crossref_primary_10_1016_j_sysarc_2010_03_005 crossref_primary_10_1007_s10589_009_9241_x crossref_primary_10_1016_j_mejo_2024_106244 crossref_primary_10_1007_s00366_009_0138_1 crossref_primary_10_1007_s11227_016_1862_0 crossref_primary_10_1016_j_ins_2019_05_028 crossref_primary_10_1016_j_swevo_2019_100580 crossref_primary_10_1016_j_procs_2015_05_489 crossref_primary_10_1016_j_ijleo_2014_03_004 crossref_primary_10_1007_s10489_017_0929_9 crossref_primary_10_1109_ACCESS_2020_2985168 crossref_primary_10_1007_s10845_011_0617_2 crossref_primary_10_1016_j_ejor_2004_08_027 crossref_primary_10_1109_TKDE_2013_131 crossref_primary_10_3233_JIFS_182627 crossref_primary_10_1016_j_knosys_2023_111027 crossref_primary_10_1109_ACCESS_2020_3010339 crossref_primary_10_1016_j_cor_2012_07_014 crossref_primary_10_1109_TCBB_2013_2 crossref_primary_10_1016_j_amc_2004_10_080 crossref_primary_10_1109_JSEN_2018_2850847 crossref_primary_10_4018_jncr_2012100102 crossref_primary_10_1016_j_engappai_2024_108127 crossref_primary_10_1016_j_asoc_2009_08_018 crossref_primary_10_1016_j_comgeo_2011_08_001 crossref_primary_10_1016_j_jmsy_2013_12_001 crossref_primary_10_3390_app10196858 crossref_primary_10_1016_j_buildenv_2024_111269 crossref_primary_10_1109_TCOMM_2023_3240697 crossref_primary_10_1007_s13218_013_0263_2 crossref_primary_10_1016_j_swevo_2015_01_002 crossref_primary_10_1007_s10696_017_9296_4 crossref_primary_10_1007_s40747_017_0057_5 crossref_primary_10_1016_j_desal_2015_11_030 crossref_primary_10_1016_j_asoc_2021_108146 crossref_primary_10_1111_mice_12385 crossref_primary_10_1155_ES_2006_54074 crossref_primary_10_21468_SciPostPhysProc_8_173 crossref_primary_10_1007_s10489_022_03883_9 crossref_primary_10_1007_s10479_006_0061_4 crossref_primary_10_1007_s10710_024_09487_1 crossref_primary_10_1002_ente_201700600 crossref_primary_10_1080_00207721_2012_745023 crossref_primary_10_1016_j_jfranklin_2019_03_035 crossref_primary_10_1109_TEVC_2005_857073 crossref_primary_10_1109_TEVC_2018_2865590 crossref_primary_10_1093_imaman_dpv010 crossref_primary_10_1109_TCYB_2020_2988896 crossref_primary_10_1016_j_swevo_2018_06_003 crossref_primary_10_1016_j_comcom_2021_01_022 crossref_primary_10_1109_TEVC_2007_904345 crossref_primary_10_1162_evco_2008_16_2_185 crossref_primary_10_1016_j_eswa_2013_11_044 crossref_primary_10_1016_j_actaastro_2017_02_023 crossref_primary_10_1002_asi_21320 crossref_primary_10_1109_TCYB_2017_2789158 crossref_primary_10_1109_TSMCA_2008_923082 crossref_primary_10_1007_s00500_023_07978_4 crossref_primary_10_1109_TEVC_2005_851274 crossref_primary_10_1007_JHEP07_2021_070 crossref_primary_10_1109_TCE_2023_3240249 crossref_primary_10_1080_03052150500035658 crossref_primary_10_1007_s00500_020_05450_1 crossref_primary_10_1007_s10586_014_0409_5 crossref_primary_10_1109_TEVC_2008_920677 crossref_primary_10_1016_j_comnet_2012_06_012 crossref_primary_10_1016_j_swevo_2017_08_003 crossref_primary_10_1109_TEVC_2023_3314152 crossref_primary_10_1109_MCI_2006_1597059 crossref_primary_10_1007_s11227_020_03183_4 crossref_primary_10_1016_j_scient_2011_08_017 crossref_primary_10_3724_SP_J_1001_2009_00305 crossref_primary_10_1007_s11704_009_0005_7 crossref_primary_10_1109_TCCN_2021_3137519 crossref_primary_10_1109_ACCESS_2020_3040752 crossref_primary_10_1162_evco_2009_17_4_17403 crossref_primary_10_3390_math12182951 crossref_primary_10_1016_j_cor_2019_01_009 crossref_primary_10_1016_j_asoc_2015_11_012 crossref_primary_10_1016_j_na_2016_03_023 crossref_primary_10_1016_j_asoc_2015_11_010 crossref_primary_10_1016_j_cor_2010_10_008 crossref_primary_10_3390_a17040135 crossref_primary_10_1080_00207721_2012_724095 crossref_primary_10_1109_TEVC_2014_2308305 crossref_primary_10_1109_TWC_2004_837447 crossref_primary_10_3233_JIFS_212242 crossref_primary_10_1016_j_energy_2017_02_174 crossref_primary_10_1109_TEVC_2016_2587808 crossref_primary_10_1007_s00521_018_3898_y crossref_primary_10_1109_TCYB_2014_2363878 crossref_primary_10_1155_2018_9697104 crossref_primary_10_1080_23302674_2023_2235814 crossref_primary_10_1109_TSC_2021_3094322 crossref_primary_10_1109_TSMC_2015_2497250 crossref_primary_10_1109_JSEN_2013_2287915 crossref_primary_10_1109_TNNLS_2015_2418739 crossref_primary_10_1109_TIP_2014_2378057 crossref_primary_10_1016_j_ress_2005_11_018 crossref_primary_10_1016_j_seppur_2024_126579 crossref_primary_10_1007_s00500_022_07358_4 crossref_primary_10_1016_j_actamat_2025_120945 crossref_primary_10_1016_j_asoc_2022_109466 crossref_primary_10_1016_j_neucom_2019_12_095 crossref_primary_10_4236_iim_2012_46036 crossref_primary_10_5004_dwt_2020_26229 crossref_primary_10_1109_TASE_2022_3148459 crossref_primary_10_1162_EVCO_a_00041 crossref_primary_10_1109_TASE_2023_3312173 crossref_primary_10_1016_j_asoc_2017_07_052 crossref_primary_10_1016_j_pmcj_2015_06_002 crossref_primary_10_1016_j_renene_2020_10_125 crossref_primary_10_1080_01605682_2024_2391516 crossref_primary_10_1007_s10732_019_09407_y crossref_primary_10_1016_j_ins_2016_06_007 crossref_primary_10_1016_j_swevo_2021_100915 crossref_primary_10_1016_j_enconman_2020_113324 crossref_primary_10_1080_17517575_2019_1605001 crossref_primary_10_1016_j_asoc_2019_105684 crossref_primary_10_1007_s11277_015_2860_x crossref_primary_10_1364_OE_471998 crossref_primary_10_1007_s11859_007_0096_7 crossref_primary_10_1080_13675567_2015_1059411 crossref_primary_10_1109_TEVC_2016_2567648 crossref_primary_10_3390_drones8060247 crossref_primary_10_1007_s10898_016_0468_7 crossref_primary_10_1016_j_est_2024_110756 crossref_primary_10_1109_TITS_2024_3515997 crossref_primary_10_1140_epjp_s13360_020_00964_x crossref_primary_10_1016_j_ins_2020_11_025 crossref_primary_10_1007_s11047_015_9529_y crossref_primary_10_1016_j_engappai_2025_110444 crossref_primary_10_5004_dwt_2018_23233 crossref_primary_10_1007_s11042_020_09471_8 crossref_primary_10_1162_EVCO_a_00024 crossref_primary_10_1049_iet_gtd_2015_0367 crossref_primary_10_1007_s10489_006_0009_z crossref_primary_10_1007_s12065_013_0103_1 crossref_primary_10_1007_s10898_018_0669_3 crossref_primary_10_1016_j_cie_2023_109258 crossref_primary_10_1016_j_jocs_2015_08_009 crossref_primary_10_1109_TEVC_2005_860766 crossref_primary_10_1109_TWC_2009_071351 crossref_primary_10_1162_evco_2008_16_3_355 crossref_primary_10_1109_TEVC_2005_860762 crossref_primary_10_1155_2013_815193 crossref_primary_10_1137_130940657 crossref_primary_10_1016_j_neucom_2016_08_032 crossref_primary_10_1109_TEVC_2004_837108 crossref_primary_10_1007_s12046_019_1200_3 crossref_primary_10_1016_j_ifacol_2019_11_308 crossref_primary_10_1016_j_ijleo_2012_08_034 crossref_primary_10_3390_app10134571 crossref_primary_10_1016_j_asoc_2017_05_012 crossref_primary_10_1115_1_4026509 crossref_primary_10_1007_s00500_016_2207_x |
| Cites_doi | 10.1109/ICEC.1994.350037 10.1145/321906.321910 10.1007/3-540-36605-9_25 10.1007/978-3-642-79034-8 10.1109/CEC.2002.1007032 10.1109/cec.2002.1004489 10.1109/4235.797969 10.1109/CEC.2002.1007035 10.1016/S0022-0000(73)80033-9 10.1145/358841.358850 10.1007/3-540-45356-3_82 10.1162/106365600568167 10.1016/0196-6774(80)90005-X 10.1162/106365600568202 10.1007/BF01840386 10.1109/4235.996017 10.1007/BF03325101 10.1109/CEC.2000.870313 10.1109/CEC.2001.934295 |
| ContentType | Journal Article |
| Copyright | 2003 INIST-CNRS Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2003 |
| Copyright_xml | – notice: 2003 INIST-CNRS – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2003 |
| DBID | RIA RIE AAYXX CITATION IQODW 7SC 7SP 8FD JQ2 L7M L~C L~D F28 FR3 |
| DOI | 10.1109/TEVC.2003.817234 |
| DatabaseName | 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 Computer and Information Systems Abstracts Technology Research Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore Digital Library (LUT) url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Applied Sciences |
| EISSN | 1941-0026 |
| EndPage | 515 |
| ExternalDocumentID | 2429414951 15200657 10_1109_TEVC_2003_817234 1237166 |
| 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-c447t-2308347cbf4530a278a457ef560e2ef7cfee8462d67c8f68f4cb9d0b5ea6999c3 |
| IEDL.DBID | RIE |
| ISSN | 1089-778X |
| IngestDate | Thu Oct 02 11:48:19 EDT 2025 Sun Sep 28 10:09:00 EDT 2025 Fri Jul 25 05:53:42 EDT 2025 Mon Jul 21 09:12:41 EDT 2025 Thu Apr 24 23:02:55 EDT 2025 Wed Oct 01 06:33:20 EDT 2025 Tue Aug 26 16:38:22 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Pareto optimum Evolutionary algorithm Evolutionary computation Multiobjective programming run-time complexity multiobjective evolutionary algorithms (MOEAs) Optimization Genetic algorithm Algorithm complexity Pareto optimality Optimality criterion Data structure Time complexity |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html CC BY 4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c447t-2308347cbf4530a278a457ef560e2ef7cfee8462d67c8f68f4cb9d0b5ea6999c3 |
| 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 |
| PQID | 884290310 |
| PQPubID | 23500 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_28575183 pascalfrancis_primary_15200657 proquest_miscellaneous_901687822 proquest_journals_884290310 crossref_primary_10_1109_TEVC_2003_817234 ieee_primary_1237166 crossref_citationtrail_10_1109_TEVC_2003_817234 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2003-10-01 |
| PublicationDateYYYYMMDD | 2003-10-01 |
| PublicationDate_xml | – month: 10 year: 2003 text: 2003-10-01 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 | 2003 |
| 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 | ref15 ref14 Mulmuley (ref24) 1994 ref11 ref10 Fonseca (ref13) Zitzler (ref27) ref1 ref17 ref19 Fisher (ref12) 1963 ref18 Thomson (ref25); 1 Balling (ref2) Jin (ref16) ref23 ref26 ref20 ref22 Deb (ref9) 2001 ref21 ref28 ref8 ref7 ref4 Bäck (ref3) 1997 ref6 ref5 |
| References_xml | – ident: ref14 doi: 10.1109/ICEC.1994.350037 – volume-title: Proc. 5th Int. Conf. Genetic Algorithms ident: ref13 article-title: Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization – ident: ref19 doi: 10.1145/321906.321910 – ident: ref15 doi: 10.1007/3-540-36605-9_25 – start-page: 1042 volume-title: Proc. Genetic and Evolutionary Computation Conf., GECCO-2001 ident: ref16 article-title: Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how? – volume-title: Computational Geometry: An Introduction Through Randomized Algorithms year: 1994 ident: ref24 – ident: ref5 doi: 10.1007/978-3-642-79034-8 – ident: ref11 doi: 10.1109/CEC.2002.1007032 – ident: ref20 doi: 10.1109/cec.2002.1004489 – ident: ref28 doi: 10.1109/4235.797969 – volume: 1 start-page: 37 volume-title: Proc. 2002 Congress on Evolutionary Computation ident: ref25 article-title: An evolutionary algorithm for the multi-objective optimization of VLSI primitive operator filters – ident: ref23 doi: 10.1109/CEC.2002.1007035 – start-page: 1079 volume-title: Proc. GECCO 2001 ident: ref2 article-title: The maximin fitness function for multi-objective evolutionary computation: Application to city planning – ident: ref6 doi: 10.1016/S0022-0000(73)80033-9 – ident: ref4 doi: 10.1145/358841.358850 – ident: ref8 doi: 10.1007/3-540-45356-3_82 – ident: ref17 doi: 10.1162/106365600568167 – ident: ref22 doi: 10.1016/0196-6774(80)90005-X – ident: ref26 doi: 10.1162/106365600568202 – volume-title: IOP and Oxford Univ. Press year: 1997 ident: ref3 – start-page: 225 volume-title: Industrial Scheduling year: 1963 ident: ref12 article-title: Probabilistic learning combinations of local job-shop scheduling rules – ident: ref21 doi: 10.1007/BF01840386 – ident: ref10 doi: 10.1109/4235.996017 – volume-title: Multi-Objective Optimization Using Evolutionary Algorithms year: 2001 ident: ref9 – start-page: 95 volume-title: Proc. EUROGEN 2001—Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems ident: ref27 article-title: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization – ident: ref7 doi: 10.1007/BF03325101 – ident: ref18 doi: 10.1109/CEC.2000.870313 – ident: ref1 doi: 10.1109/CEC.2001.934295 |
| SSID | ssj0014519 |
| Score | 2.3073504 |
| Snippet | The last decade has seen a surge of research activity on multiobjective optimization using evolutionary computation and a number of well performing algorithms... The paper also points out that multiobjective EAs using fitness based on dominance counting and identification of nondominated solutions can be improved... |
| SourceID | proquest pascalfrancis crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 503 |
| SubjectTerms | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Complexity Computer science Computer science; control theory; systems Councils Counting Data structures Dominance Evolutionary algorithms Evolutionary computation Exact sciences and technology Fitness Genetic algorithms Heuristic algorithms Nearest neighbor searches Run time (computers) Runtime Sorting Studies Surges Theoretical computing |
| Title | Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms |
| URI | https://ieeexplore.ieee.org/document/1237166 https://www.proquest.com/docview/884290310 https://www.proquest.com/docview/28575183 https://www.proquest.com/docview/901687822 |
| Volume | 7 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Xplore Digital Library (LUT) 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://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB61PcGBQgsiLRQfuCCR3Wzi2A63VbWlRaIHaNHeItuxy6NN0CaREL-esZ3sUl7iZsmO5GQe_iYz_gbguVIzzTRXMcYCzAUoRSylZhjz5FonM4XuwbN9nrPTS_pmmS-34OX6LowxxhefmYkb-lx-1eje_SqbopdFeM-2YZsLFu5qrTMGjiYlFNMXiBjFckxJJsX0YvHh2DN_TgQe1xm9dQT5niquIlK2-FFs6Gbxm2P2p83JLrwd9xmKTL5M-k5N9PdfKBz_90Xuw70BdpJ50JMHsGXqPdgdWzqQwcL34O5P_IT7sHzniF1xSBAmklVfx64VPfFl6OYb4nfSWOJLEhv1OXhOspi3rwgqHzl__3oen50RWVfEX_Mi8vqqWX3qPt60D-HyZHFxfBoPrRhiTSnvYgxUREa5VpbmWSJTLiTNubGIl0xqLNfWGEQyacW4FpYJS7UqqkTlRjKEoDp7BDt1U5vHQGxKmWA5r5jWtJJKKFol1UzZmZRCZEkE01E6pR54yl27jOvSxytJUTp5uvaZWRnkGcGL9RNfA0fHP9buO3Fs1gVJRHB0SwE2846WCncbweGoEeVg5W0pBJ7mjls1gmfrWTRPl3ORtWn6tkyFT2xlEZC_rEBAxoTDaQd_3toh3NkUED6BnW7Vm6cIhDp15C3gB2gNBME |
| linkProvider | IEEE |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB6VcgAOFFoqQmnrAxcksptNHMfhtqq23YV2D7BFe4tsx-ZVErRJJMSvZ-wku215iJslO5KTefibzPgbgBdSjhRTifQxFmA2QEl9IRTDmCdWKhhJdA-O7XPOppf0zTJebsGr9V0YrbUrPtMDO3S5_LxUjf1VNkQvi_Ce3YG7MaU0bm9rrXMGliilLadPETPyZZ-UDNLhYvLhxHF_Djge2BG9cQi5riq2JlJU-FlM28_iN9fszpvTHbjod9qWmXwdNLUcqJ-3SBz_91UewcMOeJJxqymPYUsXu7DTN3UgnY3vwoNrDIV7sHxnqV1xSBAoklVT-LYZPXGF6PoHInhSGuKKEkv5pfWdZDKuXhNUPzJ_fzb2ZzMiipy4i15EXH0sV5_rT9-qJ3B5OlmcTP2uGYOvKE1qH0MVHtFESUPjKBBhwgWNE20QMelQm0QZrRHLhDlLFDeMG6pkmgcy1oIhCFXRPmwXZaGfAjEhZZzFSc6UormQXNI8yEfSjITgPAo8GPbSyVTHVG4bZlxlLmIJ0szK0zbQjLJWnh68XD_xvWXp-MfaPSuOzbpWEh4c3VCAzbwlpsLdenDQa0TW2XmVcY7nuWVX9eB4PYsGarMuotBlU2Uhd6mtyAPylxUIyRi3SO3Zn7d2DPemi4vz7Hw2f3sA9zflhM9hu141-hBhUS2PnDX8Ai0WCA4 |
| 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=Reducing+the+Run-Time+Complexity+of+Multiobjective+EAs%3A+The+NSGA-II+and+Other+Algorithms&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Jensen%2C+M.T.&rft.date=2003-10-01&rft.issn=1089-778X&rft.volume=7&rft.issue=5&rft.spage=503&rft.epage=515&rft_id=info:doi/10.1109%2FTEVC.2003.817234&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TEVC_2003_817234 |
| 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 |