{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T13:11:38Z","timestamp":1767964298446,"version":"3.49.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T00:00:00Z","timestamp":1589155200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T00:00:00Z","timestamp":1589155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s00521-020-04846-2","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T13:02:48Z","timestamp":1589202168000},"page":"15489-15502","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cancer molecular subtype classification from hypervolume-based discrete evolutionary optimization"],"prefix":"10.1007","volume":"32","author":[{"given":"Yunhe","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaochuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,11]]},"reference":[{"issue":"1","key":"4846_CR1","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1146\/annurev.bioeng.4.020702.153438","volume":"4","author":"MJ Heller","year":"2002","unstructured":"Heller MJ (2002) Dna microarray technology: devices, systems, and applications. Ann Rev Biomed Eng 4(1):129\u2013153","journal-title":"Ann Rev Biomed Eng"},{"issue":"2","key":"4846_CR2","doi-asserted-by":"publisher","first-page":"e14579","DOI":"10.1371\/journal.pone.0014579","volume":"6","author":"O Da\u011fl\u0131yan","year":"2011","unstructured":"Da\u011fl\u0131yan O, \u00dcney-Y\u00fcksektepe F, Kavakl\u0131 IH, T\u00fcrkay M (2011) Optimization based tumor classification from microarray gene expression data. PLoS One 6(2):e14579","journal-title":"PLoS One"},{"issue":"1","key":"4846_CR3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/bioinformatics\/18.1.39","volume":"18","author":"DV Nguyen","year":"2002","unstructured":"Nguyen DV, Rocke DM (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18(1):39\u201350","journal-title":"Bioinformatics"},{"issue":"5","key":"4846_CR4","doi-asserted-by":"publisher","first-page":"e1001453","DOI":"10.1371\/journal.pmed.1001453","volume":"10","author":"L Marisa","year":"2013","unstructured":"Marisa L, de Reyni\u00e8s A, Duval A, Selves J, Gaub MP, Vescovo L, Etienne-Grimaldi M-C, Schiappa R, Guenot D, Ayadi M et al (2013) Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med 10(5):e1001453","journal-title":"PLoS Med"},{"key":"4846_CR5","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.compbiolchem.2015.03.001","volume":"56","author":"HM Alshamlan","year":"2015","unstructured":"Alshamlan HM, Badr GH, Alohali YA (2015) Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput Biol Chem 56:49\u201360","journal-title":"Comput Biol Chem"},{"key":"4846_CR6","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.neucom.2016.07.080","volume":"256","author":"L Huijuan","year":"2017","unstructured":"Huijuan L, Chen J, Yan K, Jin Q, Xue Y, Gao Z (2017) A hybrid feature selection algorithm for gene expression data classification. Neurocomputing 256:56\u201362","journal-title":"Neurocomputing"},{"issue":"3","key":"4846_CR7","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.ejor.2017.08.040","volume":"265","author":"B Ghaddar","year":"2018","unstructured":"Ghaddar B, Naoum-Sawaya J (2018) High dimensional data classification and feature selection using support vector machines. Eur J Oper Res 265(3):993\u20131004","journal-title":"Eur J Oper Res"},{"issue":"2","key":"4846_CR8","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz G et al (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464","journal-title":"Ann Stat"},{"issue":"6","key":"4846_CR9","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TCBB.2014.2323065","volume":"11","author":"A Mukhopadhyay","year":"2014","unstructured":"Mukhopadhyay A, Mandal M (2014) Identifying non-redundant gene markers from microarray data: a multiobjective variable length PSO-based approach. IEEE\/ACM Trans Comput Biol Bioinform TCBB 11(6):1170\u20131183","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform TCBB"},{"key":"4846_CR10","first-page":"460","volume":"15","author":"CSR Annavarapu","year":"2016","unstructured":"Annavarapu CSR, Dara S, Banka H (2016) Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm. EXCLI J 15:460","journal-title":"EXCLI J"},{"issue":"2","key":"4846_CR11","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/s10015-008-0533-5","volume":"13","author":"MS Mohamad","year":"2009","unstructured":"Mohamad MS, Omatu S, Deris S, Misman MF, Yoshioka M (2009) A multi-objective strategy in genetic algorithms for gene selection of gene expression data. Artif Life Robot 13(2):410\u2013413","journal-title":"Artif Life Robot"},{"key":"4846_CR12","doi-asserted-by":"crossref","unstructured":"Chakraborty G, Chakraborty B (2013) Multi-objective optimization using pareto ga for gene-selection from microarray data for disease classification. In: 2013 IEEE international conference on systems, man, and cybernetics. IEEE, pp 2629\u20132634","DOI":"10.1109\/SMC.2013.449"},{"key":"4846_CR13","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.eswa.2016.04.020","volume":"59","author":"J Lv","year":"2016","unstructured":"Lv J, Peng Q, Chen X, Sun Z (2016) A multi-objective heuristic algorithm for gene expression microarray data classification. Expert Syst Appl 59:13\u201319","journal-title":"Expert Syst Appl"},{"key":"4846_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2019.2891746","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang Y, Liu B, Ma Z, Wong K-C, Li X (2019) Nature-inspired multiobjective cancer subtype diagnosis. IEEE J Transl Eng Health Med 7:1\u201312","journal-title":"IEEE J Transl Eng Health Med"},{"key":"4846_CR15","first-page":"9304","volume":"1530","author":"Bonyadi Mohammad Reza","year":"2014","unstructured":"Reza Bonyadi Mohammad, Zbigniew Michalewicz, Boukhelifa N, Bezerianos A, Cancino W, Lutton E, Mehrdad Amirghasemi, Reza Zamani, Dymond Antoine S, Schalk Kok et al (2014) Particle swarm optimization for single objective continuous space problems: a review. Evolut Comput 1530:9304","journal-title":"Evolut Comput"},{"key":"4846_CR16","doi-asserted-by":"crossref","unstructured":"Lambora A, Gupta K, Chopra K (2019) Genetic algorithm-a literature review. In: 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon). IEEE, pp 380\u2013384","DOI":"10.1109\/COMITCon.2019.8862255"},{"issue":"2","key":"4846_CR17","first-page":"137","volume":"2","author":"S Binitha","year":"2012","unstructured":"Binitha S, Sathya SS et al (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137\u2013151","journal-title":"Int J Soft Comput Eng"},{"issue":"1","key":"4846_CR18","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/S0014-5793(00)01772-5","volume":"480","author":"A Brazma","year":"2000","unstructured":"Brazma A, Vilo J (2000) Gene expression data analysis. FEBS Lett 480(1):17\u201324","journal-title":"FEBS Lett"},{"issue":"7\u20138","key":"4846_CR19","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1007\/s00521-013-1433-8","volume":"24","author":"X Li","year":"2014","unstructured":"Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7\u20138):1867\u20131877","journal-title":"Neural Comput Appl"},{"key":"4846_CR20","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.asoc.2013.09.018","volume":"18","author":"B Xue","year":"2014","unstructured":"Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for feature selection in classification: novel initialisation and updating mechanisms. Appl Soft Comput 18:261\u2013276","journal-title":"Appl Soft Comput"},{"issue":"6","key":"4846_CR21","doi-asserted-by":"publisher","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"},{"issue":"8","key":"4846_CR22","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (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"},{"key":"4846_CR23","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1109\/TEVC.2019.2895108","volume":"23","author":"J Deng","year":"2019","unstructured":"Deng J, Zhang Q (2019) Approximating hypervolume and hypervolume contributions using polar coordinate. IEEE Trans Evolut Comput 23:913\u2013918","journal-title":"IEEE Trans Evolut Comput"},{"key":"4846_CR24","doi-asserted-by":"crossref","unstructured":"Brockhoff D, Zitzler E (2007) Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 2086\u20132093","DOI":"10.1109\/CEC.2007.4424730"},{"issue":"1","key":"4846_CR25","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1162\/EVCO_a_00009","volume":"19","author":"J Bader","year":"2011","unstructured":"Bader J, Zitzler E (2011) Hype: an algorithm for fast hypervolume-based many-objective optimization. Evolut Comput 19(1):45\u201376","journal-title":"Evolut Comput"},{"issue":"1","key":"4846_CR26","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2010","unstructured":"Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evolut Comput 15(1):4\u201331","journal-title":"IEEE Trans Evolut Comput"},{"issue":"10","key":"4846_CR27","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1073\/pnas.0409462102","volume":"102","author":"HY Chang","year":"2005","unstructured":"Chang HY, Nuyten DSA, Sneddon JB, Hastie T, Tibshirani R, S\u00f8rlie T, Dai H, He YD, van\u2019t Veer LJ, Bartelink H et al (2005) Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci 102(10):3738\u20133743","journal-title":"Proc Natl Acad Sci"},{"issue":"17","key":"4846_CR28","doi-asserted-by":"publisher","first-page":"2691","DOI":"10.1093\/bioinformatics\/btx167","volume":"33","author":"H Liu","year":"2017","unstructured":"Liu H, Zhao R, Fang H, Cheng F, Yun F, Liu Y-Y (2017) Entropy-based consensus clustering for patient stratification. Bioinformatics 33(17):2691\u20132698","journal-title":"Bioinformatics"},{"key":"4846_CR29","first-page":"e1056","volume":"10","author":"X Li","year":"2018","unstructured":"Li X, Zhang S, Wong K-C (2018) Single-cell rna-seq interpretations using evolutionary multiobjective ensemble pruning. Bioinformatics 10:e1056","journal-title":"Bioinformatics"},{"issue":"336","key":"4846_CR30","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846\u2013850","journal-title":"J Am Stat Assoc"},{"issue":"Dec","key":"4846_CR31","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J (2002) Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583\u2013617","journal-title":"J Mach Learn Res"},{"issue":"3","key":"4846_CR32","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","volume":"8","author":"CAC Coello","year":"2004","unstructured":"Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8(3):256\u2013279","journal-title":"IEEE Trans Evolut Comput"},{"issue":"12","key":"4846_CR33","doi-asserted-by":"publisher","first-page":"3529","DOI":"10.1007\/s00500-014-1565-5","volume":"19","author":"UK Sikdar","year":"2015","unstructured":"Sikdar UK, Ekbal A, Saha S (2015) Mode: multiobjective differential evolution for feature selection and classifier ensemble. Soft Comput 19(12):3529\u20133549","journal-title":"Soft Comput"},{"issue":"2","key":"4846_CR34","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evolut Comput"},{"key":"4846_CR35","unstructured":"Laumanns M (2002) SPEA2: improving the strength pareto evolutionary algorithm. Technical report gloriastrasse"},{"issue":"4","key":"4846_CR36","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evolut Comput 18(4):577\u2013601","journal-title":"IEEE Trans Evolut Comput"},{"issue":"5","key":"4846_CR37","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/21.376493","volume":"25","author":"T Den\u0153ux","year":"2008","unstructured":"Den\u0153ux T (2008) A k-nearest neighbor classification rule based on Dempster\u2013Shafer theory. IEEE Trans Syst Man Cybern 25(5):804\u2013813","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"1","key":"4846_CR38","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489\u2013501","journal-title":"Neurocomputing"},{"issue":"4","key":"4846_CR39","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2016","unstructured":"Xue B, Zhang M, Browne WN, Yao X (2016) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evolut Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evolut Comput"},{"issue":"1","key":"4846_CR40","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1109\/TGRS.2011.2159726","volume":"50","author":"S Moustakidis","year":"2011","unstructured":"Moustakidis S, Mallinis G, Koutsias N, Theocharis JB, Petridis V (2011) SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans Geosci Remote Sens 50(1):149\u2013169","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"4846_CR41","first-page":"607","volume":"88","author":"PC Cheeseman","year":"1988","unstructured":"Cheeseman PC, Self M, Kelly J, Taylor W, Freeman D, Stutz JC (1988) Bayesian classification. AAAI 88:607\u2013611","journal-title":"AAAI"},{"issue":"3","key":"4846_CR42","doi-asserted-by":"publisher","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","volume":"2","author":"T Hastie","year":"2009","unstructured":"Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349\u2013360","journal-title":"Stat Interface"},{"key":"4846_CR43","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1017\/CBO9780511623400.007","volume-title":"Viable populations for conservation","author":"R Lande","year":"1987","unstructured":"Lande R, Barrowdough G (1987) Effective population size, genetic variation, and their use in population. In: Soule M (ed) Viable populations for conservation. Cambridge University Press, Cambridge, p 87"},{"key":"4846_CR44","doi-asserted-by":"crossref","unstructured":"Alander JT (1992) On optimal population size of genetic algorithms. In: CompEuro 1992 Proceedings computer systems and software engineering. IEEE, pp 65\u201370","DOI":"10.1109\/CMPEUR.1992.218485"},{"key":"4846_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2016.01.004","volume":"27","author":"S Das","year":"2016","unstructured":"Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution-an updated survey. Swarm Evolut Comput 27:1\u201330","journal-title":"Swarm Evolut Comput"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04846-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-04846-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04846-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:59:25Z","timestamp":1620694765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-04846-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,11]]},"references-count":45,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["4846"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04846-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,11]]},"assertion":[{"value":"18 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2020","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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}