{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:34:18Z","timestamp":1773498858353,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472289"],"award-info":[{"award-number":["61472289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Science and Technology Major Project of Hubei Province","award":["2019AEA170"],"award-info":[{"award-number":["2019AEA170"]}]},{"name":"Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University","award":["ZNJC201917"],"award-info":[{"award-number":["ZNJC201917"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1007\/s12293-021-00328-7","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T06:57:56Z","timestamp":1613717876000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["MLFS-CCDE: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution"],"prefix":"10.1007","volume":"13","author":[{"given":"Haoran","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fazhi","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yilin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiteng","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"328_CR1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.asoc.2017.04.042","volume":"58","author":"M Benteng","year":"2017","unstructured":"Benteng M, Xia Y (2017) A tribe competition-based genetic algorithm for feature selection in pattern classification. Appl Soft Comput 58:328\u2013338","journal-title":"Appl Soft Comput"},{"issue":"3","key":"328_CR2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s12293-010-0045-4","volume":"2","author":"FEB Otero","year":"2010","unstructured":"Otero FEB, Freitas AA, Johnson CG (2010) A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Comput 2(3):165\u2013181","journal-title":"Memetic Comput"},{"issue":"4","key":"328_CR3","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1007\/s11766-019-3714-1","volume":"34","author":"J Yong","year":"2019","unstructured":"Yong J, He F, Li H, Zhou W (2019) A novel bat algorithm based on cross boundary learning and uniform explosion strategy. Appl Math J Chin Univ 34(4):482\u2013504","journal-title":"Appl Math J Chin Univ"},{"issue":"1","key":"328_CR4","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s41688-017-0011-0","volume":"1","author":"A Kale","year":"2017","unstructured":"Kale A, Sonavane S (2017) Hybrid feature subset selection approach for fuzzy-extreme learning machine. Data-Enabled Discov Appl 1(1):10","journal-title":"Data-Enabled Discov Appl"},{"issue":"1","key":"328_CR5","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TEVC.2019.2913831","volume":"24","author":"NB Hoai","year":"2020","unstructured":"Hoai NB, Bing X, Peter A, Hisao I, Mengjie Z (2020) Multiple reference points-based decomposition for multi-objective feature selection in classification: static and dynamic mechanisms. IEEE Trans Evol Comput 24(1):170\u2013184","journal-title":"IEEE Trans Evol Comput"},{"key":"328_CR6","first-page":"298","volume":"9592","author":"NH Bach","year":"2016","unstructured":"Bach NH, Xue B, Mengjie Z (2016) A subset similarity guided method for multi-objective feature selection. Lect Notes Artif Intell 9592:298\u2013310","journal-title":"Lect Notes Artif Intell"},{"key":"328_CR7","doi-asserted-by":"crossref","unstructured":"Dong H, Sun J, Li T, Ding R, Sun X (2020) A multi-objective algorithm for multi-label filter feature selection problem. Appl Intell (7)","DOI":"10.1007\/s10489-020-01785-2"},{"issue":"3","key":"328_CR8","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1049\/iet-net.2018.5206","volume":"9","author":"R Monika","year":"2020","unstructured":"Monika R, Yun TG, Jonathon C (2020) Multi-objective-based feature selection for ddos attack detection in iot networks. Iet Netw 9(3):120\u2013127","journal-title":"Iet Netw"},{"issue":"5","key":"328_CR9","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1109\/TCYB.2017.2702059","volume":"48","author":"K Majid","year":"2018","unstructured":"Majid K, Wael L, Narges A, Dimitrios H (2018) Feature selection for nonstationary data: application to human recognition using medical biometrics. IEEE Trans Cybern 48(5):1446\u20131459","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"328_CR10","first-page":"1","volume":"34","author":"Q Quqn","year":"2020","unstructured":"Quqn Q, He F, Li H (2020) A multi-phase blending method with incremental intensity for training detection networks. Vis Comput 34(1):1\u201326","journal-title":"Vis Comput"},{"key":"328_CR11","unstructured":"Komeili M, Louis W, Armanfard N, Hatzinakos D (2017) Feature selection for nonstationary data: Application to human recognition using medical biometrics. IEEE Trans Cybern 1\u201314"},{"key":"328_CR12","first-page":"137","volume":"16","author":"J Luo","year":"2020","unstructured":"Luo J, He F, Li H, Liang Y (2020) A novel whale optimization algorithm with filtering disturbance and non-linear step. Int J Bio-Inspir Comput 16:137\u2013148","journal-title":"Int J Bio-Inspir Comput"},{"key":"328_CR13","doi-asserted-by":"crossref","unstructured":"He CL, Zhang Y, Gong DW, Wu B (2020) Multi-objective feature selection based on artificial bee colony for hyperspectral images","DOI":"10.1007\/978-981-15-3425-6_48"},{"key":"328_CR14","doi-asserted-by":"crossref","unstructured":"Tian D (2016) A multi-objective genetic local search algorithm for optimal feature subset selection. In: 2016 International conference on computational science and computational intelligence (CSCI) (pp 1089\u20131094). IEEE","DOI":"10.1109\/CSCI.2016.0208"},{"key":"328_CR15","unstructured":"Gu S, ChengR, Jin Y (2016) Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 1\u201312"},{"key":"328_CR16","unstructured":"Ishibuchi H, Tsukamoto N, Nojima T (2008) Evolutionary many-objective optimization: a short review. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence) (pp 2419\u20132426). IEEE"},{"issue":"5","key":"328_CR17","doi-asserted-by":"crossref","first-page":"3638","DOI":"10.1016\/j.eswa.2009.10.027","volume":"37","author":"B Huang","year":"2010","unstructured":"Huang B, Buckley B, Kechadi TM (2010) Multi-objective feature selection by using nsga-ii for customer churn prediction in telecommunications. Expert Syst Appl 37(5):3638\u20133646","journal-title":"Expert Syst Appl"},{"key":"328_CR18","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.swevo.2018.02.021","volume":"42","author":"M Zawbaa Hossam","year":"2018","unstructured":"Zawbaa Hossam M, Eid E, Crina G, Vaclav S (2018) Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evolut Comput 42:29\u201342","journal-title":"Swarm Evolut Comput"},{"key":"328_CR19","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ins.2020.03.032","volume":"523","author":"AD Li","year":"2020","unstructured":"Li AD, Bing XB, Zhang M (2020) Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Inf Sci 523:245\u2013265","journal-title":"Inf Sci"},{"key":"328_CR20","doi-asserted-by":"crossref","unstructured":"Guo X, Wang X, Wang M, Wang Y (2012) A new objective reduction algorithm for many-objective problems: employing mutual information and clustering algorithm. In: 2012 Eighth international conference on computational intelligence and security (pp 11\u201316). IEEE","DOI":"10.1109\/CIS.2012.11"},{"issue":"3","key":"328_CR21","first-page":"266","volume":"95","author":"S Takfarinas","year":"2018","unstructured":"Takfarinas S, David B, Goetz B, Anthony V (2018) Is seeding a good strategy in multi-objective feature selection when feature models evolve? Inf Softw Technol 95(3):266\u2013280","journal-title":"Inf Softw Technol"},{"issue":"3","key":"328_CR22","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s00500-016-2385-6","volume":"22","author":"G Shenkai","year":"2018","unstructured":"Shenkai G, Ran C, Yaochu J (2018) Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 22(3):811\u2013822","journal-title":"Soft Comput"},{"key":"328_CR23","doi-asserted-by":"crossref","unstructured":"Binder M, MoosbauerJ, Thomas J, Bischl B (2020) Multi-objective hyperparameter tuning and feature selection using filter ensembles. In: GECCO \u201920: genetic and evolutionary computation conference","DOI":"10.1145\/3377930.3389815"},{"issue":"3","key":"328_CR24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s12293-011-0065-8","volume":"3","author":"D Konrad","year":"2011","unstructured":"Konrad D (2011) Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Comput 3(3):149\u2013162","journal-title":"Memetic Comput"},{"issue":"2","key":"328_CR25","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.eswa.2005.09.024","volume":"31","author":"CL Huang","year":"2006","unstructured":"Huang CL, Wang CJ (2006) A ga-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31(2):231\u2013240","journal-title":"Expert Syst Appl"},{"key":"328_CR26","doi-asserted-by":"crossref","unstructured":"Lin SW, Tseng TY, Chen SC, Huang JF (2006) A sa-based feature selection and parameter optimization approach for support vector machine. In: 2006 IEEE international conference on systems, man and cybernetics (vol\u00a04, pp 3144\u20133145). IEEE","DOI":"10.1109\/ICSMC.2006.384599"},{"issue":"1","key":"328_CR27","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neucom.2009.07.014","volume":"73","author":"Cheng Lung Huang","year":"2009","unstructured":"Cheng Lung Huang (2009) Aco-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing 73(1):438\u2013448","journal-title":"Neurocomputing"},{"issue":"1","key":"328_CR28","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"P Linqiang","year":"2019","unstructured":"Linqiang P, Cheng H, Ye T, Handing W, Xingyi Z, Yaochu J (2019) A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 23(1):74\u201388","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"328_CR29","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s12293-009-0008-9","volume":"1","author":"N Ferrante","year":"2009","unstructured":"Ferrante N, Ville T (2009) Scale factor local search in differential evolution. Memetic Comput 1(2):153\u2013171","journal-title":"Memetic Comput"},{"issue":"5","key":"328_CR30","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1007\/s11704-018-6442-4","volume":"13","author":"K Li","year":"2019","unstructured":"Li K, He F, Yu H, Chen X (2019) A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning. Front Comput Sci 13(5):1116\u20131135","journal-title":"Front Comput Sci"},{"key":"328_CR31","first-page":"1","volume":"5","author":"NH Bach","year":"2018","unstructured":"Bach NH, Xue B, Peter A (2018) Pso with surrogate models for feature selection: static and dynamic clustering-based methods. Memetic Comput 5:1\u201310","journal-title":"Memetic Comput"},{"issue":"1","key":"328_CR32","first-page":"22","volume":"64","author":"Z Yudong","year":"2014","unstructured":"Yudong Z, Shuihua W, Preetha P, Genlin J (2014) Binary pso with mutation operator for feature selection using decision tree applied to spam detection. Knowl-Based Syst 64(1):22\u201331","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"328_CR33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compbiolchem.2007.09.005","volume":"32","author":"CL Yeh","year":"2008","unstructured":"Yeh CL, Wei CH, Chung Jui T, Hong YC (2008) Improved binary pso for feature selection using gene expression data. Comput Biol Chem 32(1):29\u201338","journal-title":"Comput Biol Chem"},{"issue":"1","key":"328_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/106365600568086","volume":"8","author":"MA Potter","year":"2014","unstructured":"Potter MA, De Jong KA (2014) Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol Comput 8(1):1\u201329","journal-title":"Evol Comput"},{"key":"328_CR35","doi-asserted-by":"crossref","unstructured":"Antonio LM, Coello Coello CA (2016) Indicator-based cooperative coevolution for multi-objective optimization. In: Evolutionary computation","DOI":"10.1109\/CEC.2016.7743897"},{"issue":"6","key":"328_CR36","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/TEVC.2017.2767023","volume":"22","author":"LM Antonio","year":"2018","unstructured":"Antonio LM, Coello Coello CA (2018) Coevolutionary multi-objective evolutionary algorithms: a survey of the state-of-the-art. IEEE Trans Evolut Comput 22(6):851\u2013865","journal-title":"IEEE Trans Evolut Comput"},{"key":"328_CR37","unstructured":"Hammami M, Bechikh S, Hung CC, Said LB (2018) A multi-objective hybrid filter-wrapper evolutionary approach for feature selection. Memetic Comput 1\u201316"},{"issue":"2","key":"328_CR38","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1016\/j.eswa.2008.01.053","volume":"36","author":"Z Huimin","year":"2009","unstructured":"Huimin Z, Sinha Atish P, Ge W (2009) Effects of feature construction on classification performance: an empirical study in bank failure prediction. Expert Syst Appl 36(2):2633\u20132644","journal-title":"Expert Syst Appl"},{"issue":"5","key":"328_CR39","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","volume":"28","author":"D Lu","year":"2007","unstructured":"Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823\u2013870","journal-title":"Int J Remote Sens"},{"key":"328_CR40","doi-asserted-by":"crossref","unstructured":"Adler J, Parmryd I (2010) Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander\u2019s overlap coefficient. Cytometry Part A, 77a(8):733\u2013742","DOI":"10.1002\/cyto.a.20896"},{"issue":"8","key":"328_CR41","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"P Hanchuan","year":"2005","unstructured":"Hanchuan P, Fuhui L, Chris D (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"328_CR42","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/TCYB.2015.2444435","volume":"46","author":"G Karakaya","year":"2016","unstructured":"Karakaya G, Galelli S, Ahipasaoglu SD, Taormina R (2016) Identifying (quasi) equally informative subsets in feature selection problems for classification: a max-relevance min-redundancy approach. IEEE Trans Cybern 46(6):1424\u20131437","journal-title":"IEEE Trans Cybern"},{"key":"328_CR43","unstructured":"Dua D, Graff C (2017) UCI machine learning repository"},{"key":"328_CR44","unstructured":"Wang Z, Qu L, Xin J, Yang H, Gao X (2018) A unified distributed elm framework with supervised, semi-supervised and unsupervised big data learning. Memetic Comput 1\u201311"},{"issue":"1","key":"328_CR45","first-page":"489","volume":"70","author":"HG Bin","year":"2006","unstructured":"Bin HG, Zhu Qin Yu, Kheong SC (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"key":"328_CR46","doi-asserted-by":"crossref","unstructured":"Hamdani TM, Won J-M, Alimi AM, Karray F (2007) Multi-objective feature selection with nsga ii. In: International conference on adaptive and natural computing algorithms (pp 240\u2013247). Springer","DOI":"10.1007\/978-3-540-71618-1_27"},{"key":"328_CR47","doi-asserted-by":"crossref","unstructured":"Das SK, Mohanty R, Mohanty M, Mahamaya M (2020) Multi-objective feature selection (mofs) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods. Nat Hazards (11)","DOI":"10.1007\/s11069-020-04089-3"},{"key":"328_CR48","doi-asserted-by":"crossref","unstructured":"Usman AM, Yusof UK, Naim S (2020) Filter-based multi-objective feature selection using nsga iii and cuckoo optimisation algorithm. IEEE Access (99):1","DOI":"10.1109\/ACCESS.2020.2987057"},{"issue":"9","key":"328_CR49","doi-asserted-by":"crossref","first-page":"11779","DOI":"10.1007\/s11042-018-6735-5","volume":"78","author":"h Yu","year":"2019","unstructured":"Yu h, He F, Pan Y (2019) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimedia Tools Appl 78(9):11779\u201311798","journal-title":"Multimedia Tools Appl"},{"issue":"5","key":"328_CR50","doi-asserted-by":"crossref","first-page":"145316","DOI":"10.1007\/s11704-019-8184-3","volume":"14","author":"N Hou","year":"2020","unstructured":"Hou N, He F, Chen Y (2020) An efficient gpu-based parallel tabu search algorithm for hardware\/software co-design. Front Comput Sci 14(5):145316","journal-title":"Front Comput Sci"},{"issue":"3","key":"328_CR51","doi-asserted-by":"crossref","first-page":"581","DOI":"10.3233\/IDA-194641","volume":"24","author":"J Luo","year":"2020","unstructured":"Luo J, He F, Yong J (2020) An efficient and robust bat algorithm with fusion of opposition-based learning and whale optimization algorithm. Intell Data Anal 24(3):581\u2013606","journal-title":"Intell Data Anal"},{"key":"328_CR52","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1007\/s11042-019-08399-y","volume":"79","author":"J Zhang","year":"2020","unstructured":"Zhang J, He F, Chen Y (2020) A new haze removal approach for sky\/river alike scenes based on external and internal clues. Multimedia Tools Appl 79:2085\u20132107","journal-title":"Multimedia Tools Appl"},{"issue":"2","key":"328_CR53","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s12293-014-0132-z","volume":"6","author":"CC Ant\u00f3nio","year":"2014","unstructured":"Ant\u00f3nio CC (2014) A memetic algorithm based on multiple learning procedures for global optimal design of composite structures. Memetic Comput 6(2):113\u2013131","journal-title":"Memetic Comput"},{"issue":"4","key":"328_CR54","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1007\/s11280-020-00793-z","volume":"23","author":"Y Pan","year":"2020","unstructured":"Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4):2259\u20132279","journal-title":"World Wide Web"},{"issue":"4","key":"328_CR55","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s12293-015-0165-y","volume":"7","author":"H Li","year":"2015","unstructured":"Li H, Jingjing M, Maoguo G, Qiongzhi J, Licheng J (2015) Change detection in synthetic aperture radar images based on evolutionary multi-objective optimization with ensemble learning. Memetic Comput 7(4):275\u2013289","journal-title":"Memetic Comput"},{"issue":"4","key":"328_CR56","doi-asserted-by":"crossref","first-page":"417","DOI":"10.3233\/ICA-200641","volume":"27","author":"Y Liang","year":"2020","unstructured":"Liang Y, He F, Zeng X (2020) 3d mesh simplification with feature preservation based on whale optimization algorithm and differential evolution. Integrated Comput-Aided Eng 27(4):417\u2013435","journal-title":"Integrated Comput-Aided Eng"},{"issue":"9","key":"328_CR57","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1007\/s00371-019-01774-8","volume":"36","author":"S Zhang","year":"2020","unstructured":"Zhang S, He F (2020) Drcdn: learning deep residual convolutional dehazing networks. Vis Comput 36(9):1797\u20131808","journal-title":"Vis Comput"},{"issue":"2","key":"328_CR58","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s00371-018-1612-9","volume":"36","author":"S Zhang","year":"2020","unstructured":"Zhang S, He F, Ren W, Yao J (2020) Joint learning of image detail and transmission map for single image dehazing. Vis Comput 36(2):305\u2013316","journal-title":"Vis Comput"},{"key":"328_CR59","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.neucom.2018.12.025","volume":"332","author":"Y Pan","year":"2019","unstructured":"Pan Y, He F, Yu H (2019) A novel enhanced collaborative autoencoder with knowledge distillation for top-n recommender systems. Neurocomputing 332:137\u2013148","journal-title":"Neurocomputing"},{"key":"328_CR60","doi-asserted-by":"crossref","unstructured":"Chen X, He F, Yu H (2019) A matting method based on full feature coverage. Multimedia Tools Appl 78(9):11173\u201311201","DOI":"10.1007\/s11042-018-6690-1"},{"key":"328_CR61","doi-asserted-by":"crossref","first-page":"106335","DOI":"10.1016\/j.asoc.2020.106335","volume":"93","author":"Y Chen","year":"2020","unstructured":"Chen Y, He F, Li H, Zhang D, Wu Y (2020) A full migration bbo algorithm with enhanced population quality bounds for multimodal biomedical image registration. Appl Soft Comput 93:106335","journal-title":"Appl Soft Comput"},{"key":"328_CR62","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-9518-x","author":"W Cai","year":"2020","unstructured":"Cai W, He F, Lv X, Cheng Y (2020) A semi-transparent selective undo algorithm for multi-user collaborative editors. Front Comput Sci. https:\/\/doi.org\/10.1007\/s11704-020-9518-x","journal-title":"Front Comput Sci"},{"issue":"3","key":"328_CR63","doi-asserted-by":"crossref","first-page":"143301","DOI":"10.1007\/s11704-019-8123-3","volume":"14","author":"YT Pan","year":"2020","unstructured":"Pan YT, He FZ, Yu HP (2020) A correlative denoising autoencoder to model social influence for top-n recommender system. Front Comput Sci 14(3):143301","journal-title":"Front Comput Sci"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-021-00328-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12293-021-00328-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-021-00328-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T05:44:31Z","timestamp":1614231871000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12293-021-00328-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,18]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,3]]}},"alternative-id":["328"],"URL":"https:\/\/doi.org\/10.1007\/s12293-021-00328-7","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"value":"1865-9284","type":"print"},{"value":"1865-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,18]]},"assertion":[{"value":"3 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 February 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"Haoran Li declares that he has no conflict of interest. Fazhi He declares that he has no conflict of interest. Yiling Chen declares that she has no conflict of interest. Yiteng Pan declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}