An efficient optimization approach for designing machine learning models based on genetic algorithm
Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy. In this study, we present a methodology to optimize the architecture and the...
Saved in:
Published in | Neural computing & applications Vol. 33; no. 6; pp. 1923 - 1933 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.03.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy. In this study, we present a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process. The proposed approach employs genetic algorithm (GA)-based integer-valued optimization for two ML models, namely deep neural networks (DNN) and adaptive neuro-fuzzy inference system (ANFIS). The selected variables in the DNN optimization problems are the number of hidden layers, their number of neurons and their activation function, while the type and the number of membership functions are the design variables in the ANFIS optimization problem. The mean squared error (MSE) between the predictions and the target outputs is minimized as the optimization fitness function. The proposed scheme is validated through a case study of computational material design. We apply the method to predict the fracture energy of polymer/nanoparticles composites (PNCs) with a database gathered from the literature. The optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network. Also, it outperforms ANFIS with significantly lower number of generations in GA. The proposed method can be easily extended to optimize similar architecture properties of ML models in various complex systems. |
---|---|
AbstractList | Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy. In this study, we present a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process. The proposed approach employs genetic algorithm (GA)-based integer-valued optimization for two ML models, namely deep neural networks (DNN) and adaptive neuro-fuzzy inference system (ANFIS). The selected variables in the DNN optimization problems are the number of hidden layers, their number of neurons and their activation function, while the type and the number of membership functions are the design variables in the ANFIS optimization problem. The mean squared error (MSE) between the predictions and the target outputs is minimized as the optimization fitness function. The proposed scheme is validated through a case study of computational material design. We apply the method to predict the fracture energy of polymer/nanoparticles composites (PNCs) with a database gathered from the literature. The optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network. Also, it outperforms ANFIS with significantly lower number of generations in GA. The proposed method can be easily extended to optimize similar architecture properties of ML models in various complex systems. Abstract Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires further research as most procedures adopt a trial and error strategy. In this study, we present a methodology to optimize the architecture and the feature configurations of ML models considering a supervised learning process. The proposed approach employs genetic algorithm (GA)-based integer-valued optimization for two ML models, namely deep neural networks (DNN) and adaptive neuro-fuzzy inference system (ANFIS). The selected variables in the DNN optimization problems are the number of hidden layers, their number of neurons and their activation function, while the type and the number of membership functions are the design variables in the ANFIS optimization problem. The mean squared error (MSE) between the predictions and the target outputs is minimized as the optimization fitness function. The proposed scheme is validated through a case study of computational material design. We apply the method to predict the fracture energy of polymer/nanoparticles composites (PNCs) with a database gathered from the literature. The optimized DNN model shows superior prediction accuracy compared to the classical one-hidden layer network. Also, it outperforms ANFIS with significantly lower number of generations in GA. The proposed method can be easily extended to optimize similar architecture properties of ML models in various complex systems. |
Author | Hamdia, Khader M. Rabczuk, Timon Zhuang, Xiaoying |
Author_xml | – sequence: 1 givenname: Khader M. orcidid: 0000-0001-9898-8421 surname: Hamdia fullname: Hamdia, Khader M. organization: Chair of Computational Science and Simulation Technology, Department of Mathematics and Physics, Leibniz Universität Hannover – sequence: 2 givenname: Xiaoying orcidid: 0000-0001-6562-2618 surname: Zhuang fullname: Zhuang, Xiaoying email: xiaoying.zhuang@tdtu.edu.vn organization: Division of Computational Mechanics, Ton Duc Thang University, Faculty of Civil Engineering, Ton Duc Thang University – sequence: 3 givenname: Timon surname: Rabczuk fullname: Rabczuk, Timon organization: Institute of Structural Mechanics, Bauhaus-Universität Weimar |
BookMark | eNp9kEtLQzEQhYMoWKt_wFXA9dXJ4z6yLMUXCG66D2nu5DalTWpyC-qvN3oFd64GzpxzZvguyGmIAQm5ZnDLANq7DFBzVgGHCmoQdfV-QmZMClEJqLtTMgMly7qR4pxc5LwFANl09YzYRaDonLcew0jjYfR7_2lGHwM1h0OKxm6oi4n2mP0QfBjovkg-IN2hSZMQe9xlujYZe1pyAwYcvaVmN8Tkx83-kpw5s8t49TvnZPVwv1o-VS-vj8_LxUtlJVdjZbExXa14b4Ab4dataNbYqtow3tRKWMea3rZMMXDSGtfzTipmGXOiF6ZFMSc3U215--2IedTbeEyhXNRcqoYr2QleXHxy2RRzTuj0Ifm9SR-agf5mqSeWurDUPyz1ewmJKZSLOQyY_qr_SX0B0WN6yw |
CitedBy_id | crossref_primary_10_3390_app122311997 crossref_primary_10_1007_s44291_024_00007_0 crossref_primary_10_1021_acsmaterialslett_2c00734 crossref_primary_10_3390_app11178258 crossref_primary_10_3390_app14114426 crossref_primary_10_1007_s11042_023_15467_x crossref_primary_10_1016_j_asoc_2020_106734 crossref_primary_10_1016_j_envres_2023_116290 crossref_primary_10_3390_sym13030428 crossref_primary_10_1002_nme_6828 crossref_primary_10_1007_s10668_023_04257_y crossref_primary_10_1016_j_comnet_2023_110085 crossref_primary_10_3390_computers11050070 crossref_primary_10_1007_s00707_023_03691_3 crossref_primary_10_1080_01496395_2024_2330677 crossref_primary_10_1080_08956308_2023_2236475 crossref_primary_10_1002_er_7879 crossref_primary_10_1007_s00158_022_03415_6 crossref_primary_10_1016_j_cjche_2024_03_021 crossref_primary_10_3390_biomimetics8080574 crossref_primary_10_1155_2022_3106672 crossref_primary_10_12677_CSA_2023_135096 crossref_primary_10_3846_jcem_2024_21356 crossref_primary_10_1007_s11709_023_0940_7 crossref_primary_10_1007_s10660_023_09753_x crossref_primary_10_1016_j_crgsc_2022_100325 crossref_primary_10_1007_s11709_024_1039_5 crossref_primary_10_1080_21655979_2023_2244232 crossref_primary_10_20964_2021_11_10 crossref_primary_10_3934_mbe_2023512 crossref_primary_10_1016_j_tust_2023_105319 crossref_primary_10_1039_D3YA00104K crossref_primary_10_1007_s10489_022_03799_4 crossref_primary_10_3390_s20164449 crossref_primary_10_1007_s11709_024_1041_y crossref_primary_10_32604_iasc_2022_021461 crossref_primary_10_1016_j_scitotenv_2022_154124 crossref_primary_10_1007_s00466_023_02276_0 crossref_primary_10_1088_1402_4896_ad05ae crossref_primary_10_1088_2631_8695_ad2ab7 crossref_primary_10_1007_s11227_023_05775_2 crossref_primary_10_1016_j_aej_2021_04_098 crossref_primary_10_1007_s11709_021_0719_7 crossref_primary_10_1155_2021_2115653 crossref_primary_10_1016_j_istruc_2023_105173 crossref_primary_10_1007_s40722_023_00282_1 crossref_primary_10_3390_logistics5030061 crossref_primary_10_1016_j_triboint_2023_108411 crossref_primary_10_3233_AIC_230063 crossref_primary_10_1080_01691864_2024_2370507 crossref_primary_10_61186_ist_202401_01_03 crossref_primary_10_1038_s41598_023_39790_3 crossref_primary_10_1002_htj_22568 crossref_primary_10_1016_j_rinp_2023_106408 crossref_primary_10_1002_smsc_202300185 crossref_primary_10_1007_s41870_023_01725_6 crossref_primary_10_1016_j_oceaneng_2022_112839 crossref_primary_10_1007_s00521_021_06288_w crossref_primary_10_1007_s11042_023_16788_7 crossref_primary_10_3390_app122312487 crossref_primary_10_1007_s11709_022_0878_1 crossref_primary_10_3390_math12081199 crossref_primary_10_1016_j_asoc_2022_109371 crossref_primary_10_1007_s11709_020_0712_6 crossref_primary_10_23919_JSEE_2022_000031 crossref_primary_10_1002_pc_27969 crossref_primary_10_3390_ma17092074 crossref_primary_10_1007_s00466_024_02475_3 crossref_primary_10_1007_s11760_021_01990_7 crossref_primary_10_1016_j_envres_2023_118047 crossref_primary_10_1021_acs_iecr_2c04239 crossref_primary_10_1186_s40537_021_00485_z crossref_primary_10_3390_pr9101784 crossref_primary_10_3390_app13106069 crossref_primary_10_1155_2024_8316781 crossref_primary_10_1007_s12633_021_01349_0 crossref_primary_10_1063_5_0214940 crossref_primary_10_1007_s11227_022_04801_z crossref_primary_10_32604_cmc_2023_031194 crossref_primary_10_1109_TVCG_2022_3209469 crossref_primary_10_7717_peerj_cs_1860 crossref_primary_10_1007_s11709_021_0727_7 crossref_primary_10_32604_csse_2023_035149 crossref_primary_10_3390_electronics11213591 crossref_primary_10_3390_foods12030619 crossref_primary_10_3390_app11146483 crossref_primary_10_1007_s40430_023_04525_y crossref_primary_10_1016_j_cma_2024_117122 crossref_primary_10_1080_09243046_2024_2355414 crossref_primary_10_1088_2631_8695_ac7a0b crossref_primary_10_1007_s11051_022_05499_z crossref_primary_10_32604_cmc_2023_036148 crossref_primary_10_1016_j_apsadv_2023_100523 crossref_primary_10_1007_s12205_021_0378_1 crossref_primary_10_3390_s22020482 crossref_primary_10_1142_S0218348X23401485 crossref_primary_10_1016_j_fuel_2023_130457 crossref_primary_10_1080_15397734_2022_2094407 crossref_primary_10_1007_s11709_021_0717_9 crossref_primary_10_1016_j_asoc_2022_109556 crossref_primary_10_1007_s11831_021_09700_9 crossref_primary_10_1007_s11042_023_17273_x crossref_primary_10_1007_s11269_024_03879_9 crossref_primary_10_1007_s11709_024_1077_z crossref_primary_10_1098_rsta_2022_0397 crossref_primary_10_1016_j_engappai_2023_106711 crossref_primary_10_32933_ActaInnovations_43_5 crossref_primary_10_1007_s11709_024_1015_0 crossref_primary_10_1007_s40031_023_00876_1 crossref_primary_10_3390_app12199892 crossref_primary_10_1016_j_eswa_2021_115153 crossref_primary_10_12677_mos_2024_133267 crossref_primary_10_1155_2022_7792958 crossref_primary_10_32604_cmc_2023_038564 crossref_primary_10_1016_j_ijleo_2022_170470 crossref_primary_10_1016_j_geoen_2023_212618 crossref_primary_10_1061__ASCE_PS_1949_1204_0000596 crossref_primary_10_1002_cpe_6988 crossref_primary_10_1002_nme_7176 crossref_primary_10_1016_j_eswa_2023_120373 |
Cites_doi | 10.1080/19942060.2018.1482476 10.1080/19942060.2018.1502688 10.1109/TSMC.1985.6313399 10.1016/j.neucom.2017.07.028 10.1016/j.engfracmech.2012.10.027 10.1016/j.neunet.2014.09.003 10.1109/72.329697 10.1007/978-981-13-0411-8_4 10.28991/esj-2019-01197 10.1016/S0032-3861(03)00546-9 10.1016/j.compscitech.2013.11.015 10.1016/j.finel.2019.07.001 10.1007/s10853-016-0468-5 10.1007/s11831-019-09382-4 10.1155/2013/305713 10.1038/nature14539 10.1016/j.compositesb.2011.11.026 10.1016/j.amc.2009.02.044 10.1080/19942060.2018.1448896 10.1016/j.disopt.2016.01.005 10.1109/TSMCB.2003.818557 10.28991/cej-03091149 10.7551/mitpress/1090.001.0001 10.1016/j.measurement.2014.08.007 10.1016/j.commatsci.2015.02.045 10.1016/j.compositesa.2015.01.027 10.3390/w9030186 10.28991/cej-2019-03091344 10.1016/j.compscitech.2004.11.003 10.5281/zenodo.1333881 10.1080/19942060.2018.1452296 10.1016/j.jhydrol.2018.11.069 10.1016/j.commatsci.2012.01.012 10.3144/expresspolymlett.2017.52 10.28991/esj-2020-01205 10.1016/j.compstruct.2012.04.033 10.1016/j.compscitech.2016.02.012 10.1016/j.engfracmech.2017.08.002 10.1533/9780857099440 10.1007/BF00540703 10.1016/j.compscitech.2009.12.024 10.1109/21.256541 10.1016/S0045-7949(01)00039-6 10.1016/j.compscitech.2015.02.014 10.1016/j.neucom.2015.03.060 10.1002/9781119994374 |
ContentType | Journal Article |
Copyright | The Author(s) 2020 The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: The Author(s) 2020 – notice: The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | C6C AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PQEST PQQKQ PQUKI PRINS |
DOI | 10.1007/s00521-020-05035-x |
DatabaseName | SpringerOpen CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central Advanced Technologies & Aerospace Collection AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China |
DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest One Academic |
DatabaseTitleList | Advanced Technologies & Aerospace Collection CrossRef |
Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Computer Science |
EISSN | 1433-3058 |
EndPage | 1933 |
ExternalDocumentID | 10_1007_s00521_020_05035_x |
GrantInformation_xml | – fundername: Alexander von Humboldt-Stiftung grantid: Sofja Kovalevskaja 2015 funderid: http://dx.doi.org/10.13039/100005156 |
GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AABYN AAFGU AAHNG AAIAL AAJKR AANZL AAOBN AAPBV AARHV AARTL AATNV AATVU AAUYE AAWCG AAWWR AAYFA AAYIU AAYQN AAYTO ABBBX ABBXA ABDBF ABDZT ABECU ABFGW ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKAS ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABPTK ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACBMV ACBRV ACBXY ACBYP ACGFS ACHSB ACHXU ACIGE ACIPQ ACKNC ACMDZ ACMLO ACOKC ACOMO ACSNA ACTTH ACVWB ACWMK ADGRI ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMDM ADOXG ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEEQQ AEFIE AEFTE AEGAL AEGNC AEJHL AEJRE AEKMD AENEX AEOHA AEPYU AESKC AESTI AETLH AEVLU AEVTX AEXYK AEYWE AFEXP AFGCZ AFKRA AFLOW AFNRJ AFQWF AFWTZ AFZKB AGAYW AGDGC AGGBP AGGDS AGJBK AGMZJ AGQMX AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AIMYW AITGF AJBLW AJDOV AJRNO AJZVZ AKQUC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UNUBA UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AACDK AAEOY AAJBT AASML AAYXX ABAKF ACAOD ACDTI ACZOJ AEFQL AEMSY AFBBN AGQEE AGRTI AIGIU CITATION H13 DWQXO PQEST PQQKQ PQUKI PRINS |
ID | FETCH-LOGICAL-c429t-ce6a8592da02a3fb736be795a126593cf16dc71910f4cafd28491c11f3d3a7e3 |
IEDL.DBID | AGYKE |
ISSN | 0941-0643 |
IngestDate | Thu Oct 10 18:02:40 EDT 2024 Thu Sep 12 19:02:00 EDT 2024 Sat Dec 16 12:10:05 EST 2023 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 6 |
Keywords | Deep neural networks Genetic algorithm Fracture energy Optimization Polymer nanocomposites Machine learning |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c429t-ce6a8592da02a3fb736be795a126593cf16dc71910f4cafd28491c11f3d3a7e3 |
ORCID | 0000-0001-6562-2618 0000-0001-9898-8421 |
OpenAccessLink | https://proxy.k.utb.cz/login?url=http://link.springer.com/10.1007/s00521-020-05035-x |
PQID | 2496294832 |
PQPubID | 2043988 |
PageCount | 11 |
ParticipantIDs | proquest_journals_2496294832 crossref_primary_10_1007_s00521_020_05035_x springer_journals_10_1007_s00521_020_05035_x |
PublicationCentury | 2000 |
PublicationDate | 2021-03-01 |
PublicationDateYYYYMMDD | 2021-03-01 |
PublicationDate_xml | – month: 03 year: 2021 text: 2021-03-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | London |
PublicationPlace_xml | – name: London – name: Heidelberg |
PublicationTitle | Neural computing & applications |
PublicationTitleAbbrev | Neural Comput & Applic |
PublicationYear | 2021 |
Publisher | Springer London Springer Nature B.V |
Publisher_xml | – name: Springer London – name: Springer Nature B.V |
References | Thostenson, Li, Chou (CR37) 2005; 65 Sarkheyli, Zain, Sharif (CR10) 2015; 166 Shabanzadeh, Senu, Shameli, Tabar (CR13) 2013 Fotovatikhah, Herrera, Shamshirband, Kw, Faizollahzadeh Ardabili, Piran (CR16) 2018; 12 Takagi, Sugeno (CR27) 1985; 1 Zuo, Blackman, Williams, Steininger (CR48) 2015; 113 Ross (CR25) 2010 Hashemi, Rahmani (CR29) 2018; 4 Morrison, Jacobson, Sauppe, Sewell (CR28) 2016; 19 Huang, Kinloch (CR39) 1992; 27 Sharafati, Haghbin, Motta, Yaseen (CR11) 2019 Jang (CR26) 1993; 23 Zamanian, Mortezaei, Salehnia, Jam (CR46) 2013; 97 Hamdia, Rabczuk, Wahab (CR44) 2018 LeCun, Bengio, Hinton (CR6) 2015; 521 Michalski, Carbonell, Mitchell (CR5) 2013 Silani, Ziaei-Rad, Esfahanian, Tan (CR43) 2012; 94 Juang (CR7) 2004; 34 Argon, Cohen (CR36) 2003; 44 Rafiq, Bugmann, Easterbrook (CR22) 2001; 79 Hamdia, Ghasemi, Bazi, AlHichri, Alajlan, Rabczuk (CR21) 2019; 165 Faizollahzadeh Ardabili, Najafi, Shamshirband, Minaei Bidgoli, Deo, Kw (CR17) 2018; 12 Cui, Guo, Wang (CR34) 2019; 5 Carolan, Ivankovic, Kinloch, Sprenger, Taylor (CR49) 2017; 52 Fazilat, Ghatarband, Mazinani, Asadi, Shiri, Kalaee (CR12) 2012; 58 Scharnberg, de Loreto, Alves (CR35) 2020; 4 Moazenzadeh, Mohammadi, Shamshirband, Kw (CR2) 2018; 12 Deep, Singh, Kansal, Mohan (CR32) 2009; 212 Hamdia, Zhuang, He, Rabczuk (CR38) 2016; 126 Karavasilis, Tsakiroglou (CR30) 2019; 3 Yaseen, Sulaiman, Deo, Chau (CR18) 2019; 569 Suratgar, Tavakoli, Hoseinabadi (CR24) 2005; 6 Haykin (CR19) 1994 Kononenko, Kukar (CR4) 2007 Zappalorto, Pontefisso, Fabrizi, Quaresimin (CR47) 2015; 72 Najafi, Faizollahzadeh Ardabili, Shamshirband, Kw, Rabczuk (CR3) 2018; 12 Goldberg (CR33) 1989 Kw (CR1) 2017; 9 Hagan, Menhaj (CR23) 1994; 5 Holland (CR31) 1992 Msekh, Cuong, Zi, Areias, Zhuang, Rabczuk (CR45) 2018; 188 Manngård, Kronqvist, Böling (CR9) 2018; 272 Schmidhuber (CR20) 2015; 61 Williams (CR40) 2010; 70 Momeni, Nazir, Armaghani, Maizir (CR8) 2014; 57 Mesbahi, Semnani, Khorasani (CR14) 2012; 43 Quaresimin, Salviato, Zappalorto (CR41) 2014; 91 Lauke (CR42) 2017; 11 Hamdia, Lahmer, Nguyen-Thoi, Rabczuk (CR15) 2015; 102 MA Msekh (5035_CR45) 2018; 188 DE Goldberg (5035_CR33) 1989 S Faizollahzadeh Ardabili (5035_CR17) 2018; 12 RS Michalski (5035_CR5) 2013 M Zappalorto (5035_CR47) 2015; 72 D Carolan (5035_CR49) 2017; 52 K Zuo (5035_CR48) 2015; 113 S Haykin (5035_CR19) 1994 A Argon (5035_CR36) 2003; 44 AH Mesbahi (5035_CR14) 2012; 43 KM Hamdia (5035_CR44) 2018 CF Juang (5035_CR7) 2004; 34 R Moazenzadeh (5035_CR2) 2018; 12 I Kononenko (5035_CR4) 2007 P Shabanzadeh (5035_CR13) 2013 TJ Ross (5035_CR25) 2010 M Rafiq (5035_CR22) 2001; 79 T Takagi (5035_CR27) 1985; 1 KM Hamdia (5035_CR38) 2016; 126 ZM Yaseen (5035_CR18) 2019; 569 KM Hamdia (5035_CR21) 2019; 165 AA Suratgar (5035_CR24) 2005; 6 DR Morrison (5035_CR28) 2016; 19 Y Huang (5035_CR39) 1992; 27 B Najafi (5035_CR3) 2018; 12 J Schmidhuber (5035_CR20) 2015; 61 K Deep (5035_CR32) 2009; 212 M Silani (5035_CR43) 2012; 94 ET Thostenson (5035_CR37) 2005; 65 B Lauke (5035_CR42) 2017; 11 Y LeCun (5035_CR6) 2015; 521 F Fotovatikhah (5035_CR16) 2018; 12 M Karavasilis (5035_CR30) 2019; 3 JH Holland (5035_CR31) 1992 M Quaresimin (5035_CR41) 2014; 91 A Sharafati (5035_CR11) 2019 KM Hamdia (5035_CR15) 2015; 102 Chau Kw (5035_CR1) 2017; 9 MT Hagan (5035_CR23) 1994; 5 C Cui (5035_CR34) 2019; 5 E Momeni (5035_CR8) 2014; 57 H Fazilat (5035_CR12) 2012; 58 M Zamanian (5035_CR46) 2013; 97 ARA Scharnberg (5035_CR35) 2020; 4 A Sarkheyli (5035_CR10) 2015; 166 J Williams (5035_CR40) 2010; 70 JSR Jang (5035_CR26) 1993; 23 M Manngård (5035_CR9) 2018; 272 SM Hashemi (5035_CR29) 2018; 4 |
References_xml | – volume: 12 start-page: 584 issue: 1 year: 2018 end-page: 597 ident: CR2 article-title: Coupling a firefly algorithm with support vector regression to predict evaporation in northern iran publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1482476 contributor: fullname: Kw – volume: 12 start-page: 611 issue: 1 year: 2018 end-page: 624 ident: CR3 article-title: Application of ANNs, ANFIS and RSM to estimating and optimizing the parameters that affect the yield and cost of biodiesel production publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1502688 contributor: fullname: Rabczuk – volume: 1 start-page: 116 year: 1985 end-page: 132 ident: CR27 article-title: Fuzzy identification of systems and its applications to modeling and control publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/TSMC.1985.6313399 contributor: fullname: Sugeno – volume: 272 start-page: 660 year: 2018 end-page: 667 ident: CR9 article-title: Structural learning in artificial neural networks using sparse optimization publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.07.028 contributor: fullname: Böling – volume: 97 start-page: 193 year: 2013 end-page: 206 ident: CR46 article-title: Fracture toughness of epoxy polymer modified with nanosilica particles: particle size effect publication-title: Eng Fract Mech doi: 10.1016/j.engfracmech.2012.10.027 contributor: fullname: Jam – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: CR20 article-title: Deep learning in neural networks: an overview publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 contributor: fullname: Schmidhuber – volume: 5 start-page: 989 issue: 6 year: 1994 end-page: 993 ident: CR23 article-title: Training feedforward networks with the marquardt algorithm publication-title: IEEE Trans Neural Netw doi: 10.1109/72.329697 contributor: fullname: Menhaj – start-page: 41 year: 2018 end-page: 51 ident: CR44 article-title: Key parameters for fracture toughness of particle/polymer nanocomposites; sensitivity analysis via xfem modeling approach publication-title: Proceedings of the 7th International Conference on fracture, fatigue and wear doi: 10.1007/978-981-13-0411-8_4 contributor: fullname: Wahab – volume: 3 start-page: 344 issue: 6 year: 2019 end-page: 360 ident: CR30 article-title: Synthesis of aqueous suspensions of zero-valent iron nanoparticles (nZVI) from plant extracts: experimental study and numerical modeling publication-title: Emerg Sci J doi: 10.28991/esj-2019-01197 contributor: fullname: Tsakiroglou – volume: 44 start-page: 6013 issue: 19 year: 2003 end-page: 6032 ident: CR36 article-title: Toughenability of polymers publication-title: Polymer doi: 10.1016/S0032-3861(03)00546-9 contributor: fullname: Cohen – volume: 91 start-page: 16 year: 2014 end-page: 21 ident: CR41 article-title: A multi-scale and multi-mechanism approach for the fracture toughness assessment of polymer nanocomposites publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2013.11.015 contributor: fullname: Zappalorto – volume: 165 start-page: 21 year: 2019 end-page: 30 ident: CR21 article-title: A novel deep learning based method for the computational material design of flexoelectric nanostructures with topology optimization publication-title: Finite Elem Anal Des doi: 10.1016/j.finel.2019.07.001 contributor: fullname: Rabczuk – year: 1989 ident: CR33 publication-title: Genetic algorithms in search. Optimization and machine learning contributor: fullname: Goldberg – volume: 52 start-page: 1767 issue: 3 year: 2017 end-page: 1788 ident: CR49 article-title: Toughened carbon fibre-reinforced polymer composites with nanoparticle-modified epoxy matrices publication-title: J Mater Sci doi: 10.1007/s10853-016-0468-5 contributor: fullname: Taylor – year: 2019 ident: CR11 article-title: The application of soft computing models and empirical formulations for hydraulic structure scouring depth simulation: a comprehensive review, assessment and possible future research direction publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-019-09382-4 contributor: fullname: Yaseen – year: 2013 ident: CR13 article-title: Artificial intelligence in numerical modeling of silver nanoparticles prepared in montmorillonite interlayer space publication-title: J Chem doi: 10.1155/2013/305713 contributor: fullname: Tabar – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: CR6 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Hinton – volume: 43 start-page: 549 issue: 2 year: 2012 end-page: 558 ident: CR14 article-title: Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite, short carbon fibers (CF) and nano-tio2 using adaptive neuro-fuzzy inference system (ANFIS) publication-title: Compos Part B Eng doi: 10.1016/j.compositesb.2011.11.026 contributor: fullname: Khorasani – volume: 212 start-page: 505 issue: 2 year: 2009 end-page: 518 ident: CR32 article-title: A real coded genetic algorithm for solving integer and mixed integer optimization problems publication-title: Appl Math Comput doi: 10.1016/j.amc.2009.02.044 contributor: fullname: Mohan – volume: 12 start-page: 411 issue: 1 year: 2018 end-page: 437 ident: CR16 article-title: Survey of computational intelligence as basis to big flood management: challenges, research directions and future work publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1448896 contributor: fullname: Piran – year: 1994 ident: CR19 publication-title: Neural networks: a comprehensive foundation contributor: fullname: Haykin – volume: 19 start-page: 79 year: 2016 end-page: 102 ident: CR28 article-title: Branch-and-bound algorithms: a survey of recent advances in searching, branching, and pruning publication-title: Discrete Optim doi: 10.1016/j.disopt.2016.01.005 contributor: fullname: Sewell – volume: 34 start-page: 997 issue: 2 year: 2004 end-page: 1006 ident: CR7 article-title: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2003.818557 contributor: fullname: Juang – volume: 4 start-page: 2186 issue: 9 year: 2018 end-page: 2196 ident: CR29 article-title: Numerical comparison of the performance of genetic algorithm and particle swarm optimization in excavations publication-title: Civil Eng J doi: 10.28991/cej-03091149 contributor: fullname: Rahmani – year: 1992 ident: CR31 publication-title: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence doi: 10.7551/mitpress/1090.001.0001 contributor: fullname: Holland – volume: 57 start-page: 122 year: 2014 end-page: 131 ident: CR8 article-title: Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN publication-title: Measurement doi: 10.1016/j.measurement.2014.08.007 contributor: fullname: Maizir – volume: 102 start-page: 304 year: 2015 end-page: 313 ident: CR15 article-title: Predicting the fracture toughness of PNCs: a stochastic approach based on ANN and ANFIS publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2015.02.045 contributor: fullname: Rabczuk – volume: 72 start-page: 58 year: 2015 end-page: 64 ident: CR47 article-title: Mechanical behaviour of epoxy/silica nanocomposites: experiments and modelling publication-title: Compos Part A Appl Sci Manuf doi: 10.1016/j.compositesa.2015.01.027 contributor: fullname: Quaresimin – volume: 9 start-page: 186 issue: 3 year: 2017 ident: CR1 article-title: Use of meta-heuristic techniques in rainfall-runoff modelling publication-title: Water doi: 10.3390/w9030186 contributor: fullname: Kw – volume: 5 start-page: 1452 issue: 7 year: 2019 end-page: 1464 ident: CR34 article-title: Fatigue analysis for void repair of cement concrete pavement with under slab by polymer grouting publication-title: Civil Eng J doi: 10.28991/cej-2019-03091344 contributor: fullname: Wang – volume: 65 start-page: 491 issue: 3 year: 2005 end-page: 516 ident: CR37 article-title: Nanocomposites in context publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2004.11.003 contributor: fullname: Chou – volume: 6 start-page: 46 issue: 1 year: 2005 end-page: 48 ident: CR24 article-title: Modified levenberg-marquardt method for neural networks training publication-title: World Acad Sci Eng Technol doi: 10.5281/zenodo.1333881 contributor: fullname: Hoseinabadi – volume: 12 start-page: 438 issue: 1 year: 2018 end-page: 458 ident: CR17 article-title: Computational intelligence approach for modeling hydrogen production: a review publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1452296 contributor: fullname: Kw – year: 2013 ident: CR5 publication-title: Machine learning: an artificial intelligence approach contributor: fullname: Mitchell – volume: 569 start-page: 387 year: 2019 end-page: 408 ident: CR18 article-title: An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction publication-title: J Hydrol doi: 10.1016/j.jhydrol.2018.11.069 contributor: fullname: Chau – volume: 58 start-page: 31 year: 2012 end-page: 37 ident: CR12 article-title: Predicting the mechanical properties of glass fiber reinforced polymers via artificial neural network and adaptive neuro-fuzzy inference system publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2012.01.012 contributor: fullname: Kalaee – volume: 11 start-page: 545 issue: 7 year: 2017 ident: CR42 article-title: Fracture toughness modelling of polymers filled with inhomogeneously distributed rigid spherical particles publication-title: Express Polym Lett doi: 10.3144/expresspolymlett.2017.52 contributor: fullname: Lauke – volume: 4 start-page: 11 issue: 1 year: 2020 end-page: 17 ident: CR35 article-title: Optical and structural characterization of Bi2FexNbO7 nanoparticles for environmental applications publication-title: Emerg Sci J doi: 10.28991/esj-2020-01205 contributor: fullname: Alves – volume: 94 start-page: 3142 issue: 11 year: 2012 end-page: 3148 ident: CR43 article-title: On the experimental and numerical investigation of clay/epoxy nanocomposites publication-title: Compos Struct doi: 10.1016/j.compstruct.2012.04.033 contributor: fullname: Tan – volume: 126 start-page: 122 year: 2016 end-page: 129 ident: CR38 article-title: Fracture toughness of polymeric particle nanocomposites: evaluation of models performance using bayesian method publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2016.02.012 contributor: fullname: Rabczuk – volume: 188 start-page: 287 year: 2018 end-page: 299 ident: CR45 article-title: Fracture properties prediction of clay/epoxy nanocomposites with interphase zones using a phase field model publication-title: Eng Fract Mech doi: 10.1016/j.engfracmech.2017.08.002 contributor: fullname: Rabczuk – year: 2007 ident: CR4 publication-title: Machine learning and data mining: introduction to principles and algorithms doi: 10.1533/9780857099440 contributor: fullname: Kukar – volume: 27 start-page: 2763 issue: 10 year: 1992 end-page: 2769 ident: CR39 article-title: Modelling of the toughening mechanisms in rubber-modified epoxy polymers. part II a quantitative description of the microstructure-fracture property relationships publication-title: J Mater Sci doi: 10.1007/BF00540703 contributor: fullname: Kinloch – volume: 70 start-page: 885 issue: 6 year: 2010 end-page: 891 ident: CR40 article-title: Particle toughening of polymers by plastic void growth publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2009.12.024 contributor: fullname: Williams – volume: 23 start-page: 665 issue: 3 year: 1993 end-page: 685 ident: CR26 article-title: Anfis: adaptive-network-based fuzzy inference system publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/21.256541 contributor: fullname: Jang – volume: 79 start-page: 1541 issue: 17 year: 2001 end-page: 1552 ident: CR22 article-title: Neural network design for engineering applications publication-title: Comput Struct doi: 10.1016/S0045-7949(01)00039-6 contributor: fullname: Easterbrook – volume: 113 start-page: 9 year: 2015 end-page: 18 ident: CR48 article-title: The mechanical behaviour of ZnO nano-particle modified styrene acrylonitrile copolymers publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2015.02.014 contributor: fullname: Steininger – volume: 166 start-page: 357 year: 2015 end-page: 366 ident: CR10 article-title: Robust optimization of ANFIS based on a new modified GA publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.03.060 contributor: fullname: Sharif – year: 2010 ident: CR25 publication-title: Fuzzy logic with engineering applications doi: 10.1002/9781119994374 contributor: fullname: Ross – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 5035_CR6 publication-title: Nature doi: 10.1038/nature14539 contributor: fullname: Y LeCun – volume: 61 start-page: 85 year: 2015 ident: 5035_CR20 publication-title: Neural Netw doi: 10.1016/j.neunet.2014.09.003 contributor: fullname: J Schmidhuber – volume: 12 start-page: 584 issue: 1 year: 2018 ident: 5035_CR2 publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1482476 contributor: fullname: R Moazenzadeh – volume: 27 start-page: 2763 issue: 10 year: 1992 ident: 5035_CR39 publication-title: J Mater Sci doi: 10.1007/BF00540703 contributor: fullname: Y Huang – volume: 43 start-page: 549 issue: 2 year: 2012 ident: 5035_CR14 publication-title: Compos Part B Eng doi: 10.1016/j.compositesb.2011.11.026 contributor: fullname: AH Mesbahi – volume: 212 start-page: 505 issue: 2 year: 2009 ident: 5035_CR32 publication-title: Appl Math Comput doi: 10.1016/j.amc.2009.02.044 contributor: fullname: K Deep – volume: 12 start-page: 411 issue: 1 year: 2018 ident: 5035_CR16 publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1448896 contributor: fullname: F Fotovatikhah – volume: 58 start-page: 31 year: 2012 ident: 5035_CR12 publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2012.01.012 contributor: fullname: H Fazilat – volume: 3 start-page: 344 issue: 6 year: 2019 ident: 5035_CR30 publication-title: Emerg Sci J doi: 10.28991/esj-2019-01197 contributor: fullname: M Karavasilis – volume: 4 start-page: 2186 issue: 9 year: 2018 ident: 5035_CR29 publication-title: Civil Eng J doi: 10.28991/cej-03091149 contributor: fullname: SM Hashemi – volume-title: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence year: 1992 ident: 5035_CR31 doi: 10.7551/mitpress/1090.001.0001 contributor: fullname: JH Holland – volume: 4 start-page: 11 issue: 1 year: 2020 ident: 5035_CR35 publication-title: Emerg Sci J doi: 10.28991/esj-2020-01205 contributor: fullname: ARA Scharnberg – volume: 126 start-page: 122 year: 2016 ident: 5035_CR38 publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2016.02.012 contributor: fullname: KM Hamdia – volume: 113 start-page: 9 year: 2015 ident: 5035_CR48 publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2015.02.014 contributor: fullname: K Zuo – volume: 72 start-page: 58 year: 2015 ident: 5035_CR47 publication-title: Compos Part A Appl Sci Manuf doi: 10.1016/j.compositesa.2015.01.027 contributor: fullname: M Zappalorto – volume-title: Machine learning: an artificial intelligence approach year: 2013 ident: 5035_CR5 contributor: fullname: RS Michalski – volume: 57 start-page: 122 year: 2014 ident: 5035_CR8 publication-title: Measurement doi: 10.1016/j.measurement.2014.08.007 contributor: fullname: E Momeni – volume: 6 start-page: 46 issue: 1 year: 2005 ident: 5035_CR24 publication-title: World Acad Sci Eng Technol doi: 10.5281/zenodo.1333881 contributor: fullname: AA Suratgar – volume: 70 start-page: 885 issue: 6 year: 2010 ident: 5035_CR40 publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2009.12.024 contributor: fullname: J Williams – volume: 188 start-page: 287 year: 2018 ident: 5035_CR45 publication-title: Eng Fract Mech doi: 10.1016/j.engfracmech.2017.08.002 contributor: fullname: MA Msekh – volume-title: Neural networks: a comprehensive foundation year: 1994 ident: 5035_CR19 contributor: fullname: S Haykin – volume: 34 start-page: 997 issue: 2 year: 2004 ident: 5035_CR7 publication-title: IEEE Trans Syst Man Cybern Part B (Cybern) doi: 10.1109/TSMCB.2003.818557 contributor: fullname: CF Juang – volume: 12 start-page: 611 issue: 1 year: 2018 ident: 5035_CR3 publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1502688 contributor: fullname: B Najafi – volume: 102 start-page: 304 year: 2015 ident: 5035_CR15 publication-title: Comput Mater Sci doi: 10.1016/j.commatsci.2015.02.045 contributor: fullname: KM Hamdia – year: 2013 ident: 5035_CR13 publication-title: J Chem doi: 10.1155/2013/305713 contributor: fullname: P Shabanzadeh – volume: 12 start-page: 438 issue: 1 year: 2018 ident: 5035_CR17 publication-title: Eng Appl Comput Fluid Mech doi: 10.1080/19942060.2018.1452296 contributor: fullname: S Faizollahzadeh Ardabili – volume: 5 start-page: 989 issue: 6 year: 1994 ident: 5035_CR23 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.329697 contributor: fullname: MT Hagan – volume: 569 start-page: 387 year: 2019 ident: 5035_CR18 publication-title: J Hydrol doi: 10.1016/j.jhydrol.2018.11.069 contributor: fullname: ZM Yaseen – volume: 23 start-page: 665 issue: 3 year: 1993 ident: 5035_CR26 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/21.256541 contributor: fullname: JSR Jang – volume: 44 start-page: 6013 issue: 19 year: 2003 ident: 5035_CR36 publication-title: Polymer doi: 10.1016/S0032-3861(03)00546-9 contributor: fullname: A Argon – volume-title: Machine learning and data mining: introduction to principles and algorithms year: 2007 ident: 5035_CR4 doi: 10.1533/9780857099440 contributor: fullname: I Kononenko – start-page: 41 volume-title: Proceedings of the 7th International Conference on fracture, fatigue and wear year: 2018 ident: 5035_CR44 doi: 10.1007/978-981-13-0411-8_4 contributor: fullname: KM Hamdia – volume: 79 start-page: 1541 issue: 17 year: 2001 ident: 5035_CR22 publication-title: Comput Struct doi: 10.1016/S0045-7949(01)00039-6 contributor: fullname: M Rafiq – volume: 94 start-page: 3142 issue: 11 year: 2012 ident: 5035_CR43 publication-title: Compos Struct doi: 10.1016/j.compstruct.2012.04.033 contributor: fullname: M Silani – volume: 5 start-page: 1452 issue: 7 year: 2019 ident: 5035_CR34 publication-title: Civil Eng J doi: 10.28991/cej-2019-03091344 contributor: fullname: C Cui – volume: 1 start-page: 116 year: 1985 ident: 5035_CR27 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/TSMC.1985.6313399 contributor: fullname: T Takagi – volume: 11 start-page: 545 issue: 7 year: 2017 ident: 5035_CR42 publication-title: Express Polym Lett doi: 10.3144/expresspolymlett.2017.52 contributor: fullname: B Lauke – year: 2019 ident: 5035_CR11 publication-title: Arch Comput Methods Eng doi: 10.1007/s11831-019-09382-4 contributor: fullname: A Sharafati – volume: 65 start-page: 491 issue: 3 year: 2005 ident: 5035_CR37 publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2004.11.003 contributor: fullname: ET Thostenson – volume-title: Fuzzy logic with engineering applications year: 2010 ident: 5035_CR25 doi: 10.1002/9781119994374 contributor: fullname: TJ Ross – volume: 91 start-page: 16 year: 2014 ident: 5035_CR41 publication-title: Compos Sci Technol doi: 10.1016/j.compscitech.2013.11.015 contributor: fullname: M Quaresimin – volume: 97 start-page: 193 year: 2013 ident: 5035_CR46 publication-title: Eng Fract Mech doi: 10.1016/j.engfracmech.2012.10.027 contributor: fullname: M Zamanian – volume: 9 start-page: 186 issue: 3 year: 2017 ident: 5035_CR1 publication-title: Water doi: 10.3390/w9030186 contributor: fullname: Chau Kw – volume: 165 start-page: 21 year: 2019 ident: 5035_CR21 publication-title: Finite Elem Anal Des doi: 10.1016/j.finel.2019.07.001 contributor: fullname: KM Hamdia – volume: 166 start-page: 357 year: 2015 ident: 5035_CR10 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.03.060 contributor: fullname: A Sarkheyli – volume-title: Genetic algorithms in search. Optimization and machine learning year: 1989 ident: 5035_CR33 contributor: fullname: DE Goldberg – volume: 19 start-page: 79 year: 2016 ident: 5035_CR28 publication-title: Discrete Optim doi: 10.1016/j.disopt.2016.01.005 contributor: fullname: DR Morrison – volume: 272 start-page: 660 year: 2018 ident: 5035_CR9 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.07.028 contributor: fullname: M Manngård – volume: 52 start-page: 1767 issue: 3 year: 2017 ident: 5035_CR49 publication-title: J Mater Sci doi: 10.1007/s10853-016-0468-5 contributor: fullname: D Carolan |
SSID | ssj0004685 |
Score | 2.628553 |
Snippet | Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and requires... Abstract Machine learning (ML) methods have shown powerful performance in different application. Nonetheless, designing ML models remains a challenge and... |
SourceID | proquest crossref springer |
SourceType | Aggregation Database Publisher |
StartPage | 1923 |
SubjectTerms | Adaptive systems Artificial Intelligence Artificial neural networks Complex systems Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Design optimization Error analysis Fuzzy logic Genetic algorithms Image Processing and Computer Vision Machine learning Model accuracy Nanoparticles Optimization Original Article Polymer matrix composites Probability and Statistics in Computer Science |
SummonAdditionalLinks | – databaseName: AUTh Library subscriptions: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1NSwMxEB20vXjxW6xWycGbBneT_cpJqrQUD0WkQm9LNsnqwW6rXaE_30matSroNUuy8JLMTJI38wAuClGmUhhJJReSRkzFVIrQ0FRaASQuWaYdQXaUDJ-i-0k88RduC0-rbGyiM9R6puwd-TUeExImIlyAN_M3alWj7Ouql9DYhDazlZta0L7tjx4ev2VGOlFOPMNYfk_EfdqMS56zN6LYyuxjsC3cuPzpmtbx5q8nUud5Bruw7UNG0lvN8R5smGofdho5BuJ35wGoXkWMqwiBjoTM0BZMfZIlaSqHEwxRiXakDfwVmTompSFeOgIbrC7OgljXpgn2w9VlkxyJfH1GLOqX6SGMB_3x3ZB6DQWq0NPUVJlEZrFgWgZM8rJIeVKYVMQyZEksuCrDRKsUD21BGSlZavRWIlRhWHLNZWr4EbSqWWWOgYgYYymFEUpogqgsygK7ZEkWaAzhCrQEHbhs0Mvnq0oZ-VdNZId1jljnDut82YFuA3Dud80iX89xB64a0Nef_x7t5P_RTmGLWSqKo451oVW_f5gzjCXq4twvmE93accI priority: 102 providerName: ProQuest |
Title | An efficient optimization approach for designing machine learning models based on genetic algorithm |
URI | https://link.springer.com/article/10.1007/s00521-020-05035-x https://www.proquest.com/docview/2496294832 |
Volume | 33 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NT8IwFH9RuHgRPyOKpAdvOsLaffUIyEc0IcZAgqel6zo1ChgYifGv97Vs4OeBy5a0a5e1r32_t_7eewAXEU98wZWwBOPCcqh0LcFtZflCJ0BiggaxIcj2vd7QuRm5o7UftyG75yeSZqNe-brpH5ho-VJ9dqvjLCJwLGIJcwtQbHQfbttf3CFNJk40XDSpx2GZr8zfvXzXR2uQ-eNc1KibTgkGudPOkmXyUlukUU1-_I7huMmX7MFuBj9JYykv-7ClJgdQylM7kGylH4JsTIgy0SVQKZEp7ivjzGGT5FHICcJdEhsCCL6djA0rU5EsDQUW6Bw7c6LVZEywHUqqdpgk4vVxOntOn8ZHMOi0B62eleVjsCRqrdSSyhOBy2ks6lSwJPKZFymfu8KmnsuZTGwvlj4agPXEkSKJUfNxW9p2wmImfMWOoTCZTtQJEO4iLpOIdmxVd5IoibBJ4AX1GOFghLtKGS7zSQnfllE3wlV8ZTN8IQ5faIYvfC9DJZ-3MFuB8xDNSo9yBzesMlzl87Cu_r-3080eP4MdqmkuhpZWgUI6W6hzxClpVIXtoNOtonQ2r5udaialeG-2-3f3WNvyWngd0sYnylPk3w |
link.rule.ids | 315,786,790,12786,21409,27946,27947,33397,33768,41105,41144,41547,42174,42213,42616,43624,43829,51600,52135,52258 |
linkProvider | Springer Nature |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LTwMhECZaD3rxbaxW5eBNiV3YFyfTGGvV2lNNeiMsDz3YbbVr0p_vgKxVE72ygU0-YGaAb-ZD6LTgNpPcSCIZlySmKiGSR4Zk0gkgMUlz7Qmyg7T3GN-NklG4cJsFWmVtE72h1hPl7sgv4JiQUh7DArycvhKnGuVeV4OExjJaicESu9r5effmW16kl-SEE4xj98QsJM341Dl3Hwqt1D0Fu7KN85-OaRFt_nog9X6nu4nWQ8CIO58zvIWWTLmNNmoxBhz25g5SnRIbXw8C3AiegCUYhxRLXNcNxxCgYu0pG_ArPPY8SoODcAQ0OFWcGXaOTWPoB2vLpThi-fIESFTP41007F4Pr3okKCgQBX6mIsqkMk841bJNJbNFxtLCZDyREU0TzpSNUq0yOLK1bayk1eCreKSiyDLNZGbYHmqUk9LsI8wTiKQUxCeRace2sAV0ydO8rSGAK8AONNFZjZ6YftbJEF8VkT3WArAWHmsxb6JWDbAIe2YmFjPcROc16IvPf4928P9oJ2i1N3zoi_7t4P4QrVFHSvEkshZqVG_v5giiiqo49kvnA4IlyI0 |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07T8MwELYQSIiFN6JQwAMbRE3sxInHqlCVhyqGInWzHD8AiaZVGyR-Pmc3aQuCgTWJHeVi-76zv-8Oocuc21RyIwNJuQxiopJA8sgEqXQFkKgkmfYE2T7rPcf3w2S4ouL3bPf6SHKuaXBZmoqyNdG2tRC-ud1MCIOJO8h1SRcBRW7EAO1d9vwO66woI31RTohhHL8nppVs5vc-vrumJd78cUTqPU93F21XkBG35_94D62ZYh_t1OUYcDU7D5BqF9j4jBDwIXgMa8GoElniOnM4BoiKtSdtwKvwyDMpDa5KR8AFVxdnhp1r0xjawehyIkcs31_G07fydXSIBt3bQacXVDUUAgWepgyUYTJLONEyJJLaPKUsNylPZERYwqmyEdMqhaAttLGSVoO34pGKIks1lamhR2i9GBfmGGGeAJZSgFAiE8Y2tzk0yVgWaoBwOawEDXRVW09M5pkyxCInsre1AFsLb2vx2UDN2sCimjUzAaEgIzyGRaaBrmujL2__3dvJ_x6_QJtPN13xeNd_OEVbxLFUPKusidbL6Yc5A5hR5ud-JH0BxgbJ-g |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+efficient+optimization+approach+for+designing+machine+learning+models+based+on+genetic+algorithm&rft.jtitle=Neural+computing+%26+applications&rft.au=Hamdia%2C+Khader+M.&rft.au=Zhuang%2C+Xiaoying&rft.au=Rabczuk%2C+Timon&rft.date=2021-03-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=33&rft.issue=6&rft.spage=1923&rft.epage=1933&rft_id=info:doi/10.1007%2Fs00521-020-05035-x&rft.externalDocID=10_1007_s00521_020_05035_x |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |