{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T20:38:06Z","timestamp":1779309486768,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2017,10,9]],"date-time":"2017-10-09T00:00:00Z","timestamp":1507507200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s11063-017-9718-z","type":"journal-article","created":{"date-parts":[[2017,10,9]],"date-time":"2017-10-09T11:52:13Z","timestamp":1507549933000},"page":"993-1009","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Evolutionary Multi-task Learning for Modular Knowledge Representation in Neural Networks"],"prefix":"10.1007","volume":"47","author":[{"given":"Rohitash","family":"Chandra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhishek","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yew-Soon","family":"Ong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi-Keong","family":"Goh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,9]]},"reference":[{"issue":"18","key":"9718_CR1","doi-asserted-by":"crossref","first-page":"7641","DOI":"10.1073\/pnas.1018985108","volume":"108","author":"DS Bassett","year":"2011","unstructured":"Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST (2011) Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci 108(18):7641\u20137646","journal-title":"Proc Natl Acad Sci"},{"key":"9718_CR2","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/neuro.11.037.2009","volume":"3","author":"D Meunier","year":"2009","unstructured":"Meunier D, Lambiotte R, Fornito A, Ersche KD, Bullmore ET (2009) Hierarchical modularity in human brain functional networks. Front. Neuroinformatics 3:37. doi: 10.3389\/neuro.11.037.2009","journal-title":"Front. Neuroinformatics"},{"key":"9718_CR3","doi-asserted-by":"publisher","unstructured":"Nicolini C, Bifone A (2016) Modular structure of brain functional networks: breaking the resolution limit by surprise. Sci Rep 6. doi: 10.1038\/srep19250 (2016)","DOI":"10.1038\/srep19250"},{"issue":"67","key":"9718_CR4","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/S0893-6080(05)80155-8","volume":"7","author":"BL Happel","year":"1994","unstructured":"Happel BL, Murre JM (1994) Design and evolution of modular neural network architectures. Neural Netw 7(67):985\u20131004 (Models of neurodynamics and behavior)","journal-title":"Neural Netw"},{"issue":"2","key":"9718_CR5","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/72.914525","volume":"12","author":"SW Moon","year":"2001","unstructured":"Moon SW, Kong SG (2001) Block-based neural networks. IEEE Trans Neural Netw 12(2):307\u2013317","journal-title":"IEEE Trans Neural Netw"},{"issue":"8","key":"9718_CR6","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1109\/TCYB.2013.2283296","volume":"44","author":"PP San","year":"2014","unstructured":"San PP, Ling SH, Nguyen H (2014) Evolvable rough-block-based neural network and its biomedical application to hypoglycemia detection system. IEEE Trans Cybern 44(8):1338\u20131349","journal-title":"IEEE Trans Cybern"},{"key":"9718_CR7","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.neucom.2014.03.018","volume":"140","author":"VP Nambiar","year":"2014","unstructured":"Nambiar VP, Khalil-Hani M, Sahnoun R, Marsono M (2014) Hardware implementation of evolvable block-based neural networks utilizing a cost efficient sigmoid-like activation function. Neurocomputing 140:228\u2013241","journal-title":"Neurocomputing"},{"key":"9718_CR8","doi-asserted-by":"publisher","unstructured":"Clune J, Mouret JB, Lipson H (2003) The evolutionary origins of modularity. Proc R Soc Lond B Biol Sci 280(1755). doi: 10.1098\/rspb.2012.2863","DOI":"10.1098\/rspb.2012.2863"},{"issue":"4","key":"9718_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1004128","volume":"11","author":"KO Ellefsen","year":"2015","unstructured":"Ellefsen KO, Mouret JB, Clune J (2015) Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLoS Comput Biol 11(4):1\u201324","journal-title":"PLoS Comput Biol"},{"issue":"13","key":"9718_CR10","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.neucom.2010.03.021","volume":"74","author":"J Misra","year":"2010","unstructured":"Misra J, Saha I (2010) Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing 74(13):239\u2013255 (Artificial brains)","journal-title":"Neurocomputing"},{"issue":"1","key":"9718_CR11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"issue":"1","key":"9718_CR12","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/72.265960","volume":"5","author":"P Angeline","year":"1994","unstructured":"Angeline P, Saunders G, Pollack J (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5(1):54\u201365","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"9718_CR13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1162\/evco.1997.5.4.373","volume":"5","author":"DE Moriarty","year":"1997","unstructured":"Moriarty DE, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evolut Comput 5(4):373\u2013399","journal-title":"Evolut Comput"},{"issue":"4","key":"9718_CR14","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.dss.2009.05.016","volume":"47","author":"P Cortez","year":"2009","unstructured":"Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547\u2013553","journal-title":"Decis Support Syst"},{"key":"9718_CR15","doi-asserted-by":"crossref","unstructured":"Chandra R, Gupta A, Ong YS, Goh CK (2016) Evolutionary multi-task learning for modular training of feedforward neural networks. In: Neural information processing\u201423rd international conference, ICONIP 2016, Kyoto, Japan, October 16\u201321, 2016, Proceedings, Part II. 37\u201346","DOI":"10.1007\/978-3-319-46672-9_5"},{"issue":"3","key":"9718_CR16","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1086\/209962","volume":"18","author":"A Lindbeck","year":"2000","unstructured":"Lindbeck A, Snower DJ (2000) Multitask learning and the reorganization of work: from tayloristic to holistic organization. J Labor Econ 18(3):353\u2013376","journal-title":"J Labor Econ"},{"key":"9718_CR17","first-page":"1817","volume":"6","author":"RK Ando","year":"2005","unstructured":"Ando RK, Zhang T (2005) A framework for learning predictive structures from multiple tasks and unlabeled data. J Mach Learn Res 6:1817\u20131853","journal-title":"J Mach Learn Res"},{"key":"9718_CR18","first-page":"745","volume-title":"Advances in neural information processing systems","author":"L Jaco","year":"2009","unstructured":"Jaco L, philippe Vert J, Bach FR (2009) Clustered multi-task learning: a convex formulation. In: Koller D, Schuurmans D, Bengio Y, Bottou L (eds) Advances in neural information processing systems, vol 21. Curran Associates, Inc., Dutchess, pp 745\u2013752"},{"key":"9718_CR19","doi-asserted-by":"crossref","unstructured":"Chen J, Tang L, Liu J, Ye J (2009) A convex formulation for learning shared structures from multiple tasks. In: Proceedings of the 26th annual international conference on machine learning. ICML \u201909, New York, NY, USA, ACM 137\u2013144","DOI":"10.1145\/1553374.1553392"},{"issue":"4","key":"9718_CR20","doi-asserted-by":"crossref","first-page":"22:1","DOI":"10.1145\/2086737.2086742","volume":"5","author":"J Chen","year":"2012","unstructured":"Chen J, Liu J, Ye J (2012) Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Trans Knowl Discov Data 5(4):22:1\u201322:31","journal-title":"ACM Trans Knowl Discov Data"},{"key":"9718_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Yeung DY (2010) Transfer metric learning by learning task relationships. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. KDD \u201910, New York, NY, USA, ACM, pp 1199\u20131208","DOI":"10.1145\/1835804.1835954"},{"key":"9718_CR22","first-page":"83","volume":"4","author":"B Bakker","year":"2003","unstructured":"Bakker B, Heskes T (2003) Task clustering and gating for bayesian multitask learning. J Mach Learn Res 4:83\u201399","journal-title":"J Mach Learn Res"},{"key":"9718_CR23","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2015.12.092","volume":"189","author":"S Zhong","year":"2016","unstructured":"Zhong S, Pu J, Jiang YG, Feng R, Xue X (2016) Flexible multi-task learning with latent task grouping. Neurocomputing 189:179\u2013188","journal-title":"Neurocomputing"},{"key":"9718_CR24","doi-asserted-by":"publisher","unstructured":"Yuan H, Paskov I, Paskov H, Gonz\u00e1lez AJ, Leslie CS (2016) Multitask learning improves prediction of cancer drug sensitivity. Sci. Rep 6. doi: 10.1038\/srep31619","DOI":"10.1038\/srep31619"},{"issue":"1","key":"9718_CR25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/S0167-9236(00)00086-5","volume":"30","author":"RS Sexton","year":"2000","unstructured":"Sexton RS, Dorsey RE (2000) Reliable classification using neural networks: a genetic algorithm and backpropagation comparison. Decis Support Syst 30(1):11\u201322","journal-title":"Decis Support Syst"},{"issue":"5","key":"9718_CR26","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/TSMCB.2005.847740","volume":"35","author":"E Cant-Paz","year":"2005","unstructured":"Cant-Paz E, Kamath C (2005) An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems. IEEE Trans Syst Man Cybern B Cybern 35(5):915\u2013933","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"issue":"3","key":"9718_CR27","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1109\/TNN.2003.810618","volume":"14","author":"N Garcia-Pedrajas","year":"2003","unstructured":"Garcia-Pedrajas N, Hervas-Martinez C, Munoz-Perez J (2003) COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Trans Neural Netw 14(3):575\u2013596","journal-title":"IEEE Trans Neural Netw"},{"key":"9718_CR28","first-page":"937","volume":"9","author":"F Gomez","year":"2008","unstructured":"Gomez F, Schmidhuber J, Miikkulainen R (2008) Accelerated neural evolution through cooperatively coevolved synapses. J Mach Learn Res 9:937\u2013965","journal-title":"J Mach Learn Res"},{"key":"9718_CR29","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","volume":"26","author":"R Chandra","year":"2015","unstructured":"Chandra R (2015) Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans Neural Netw Learn Syst 26:3123\u20133136","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"9718_CR30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evolut Comput 10(2):99\u2013127","journal-title":"Evolut Comput"},{"issue":"4","key":"9718_CR31","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jalgor.2009.04.002","volume":"64","author":"V Heidrich-Meisner","year":"2009","unstructured":"Heidrich-Meisner V, Igel C (2009) Neuroevolution strategies for episodic reinforcement learning. J Algorithms 64(4):152\u2013168 (Special issue: reinforcement learning)","journal-title":"J Algorithms"},{"issue":"3","key":"9718_CR32","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1109\/TEVC.2015.2458037","volume":"20","author":"A Gupta","year":"2016","unstructured":"Gupta A, Ong YS, Feng L (2016) Multifactorial evolution: toward evolutionary multitasking. IEEE Trans Evolut Comput 20(3):343\u2013357","journal-title":"IEEE Trans Evolut Comput"},{"key":"9718_CR33","doi-asserted-by":"crossref","unstructured":"Gupta A, Ong YS, Feng L, Tan KC (2016) Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans Cybern (accepted)","DOI":"10.1109\/TEVC.2015.2458037"},{"issue":"2","key":"9718_CR34","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s12559-016-9395-7","volume":"8","author":"YS Ong","year":"2016","unstructured":"Ong YS, Gupta A (2016) Evolutionary multitasking: a computer science view of cognitive multitasking. Cognit Comput 8(2):125\u2013142","journal-title":"Cognit Comput"},{"issue":"10","key":"9718_CR35","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"5","key":"9718_CR36","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1109\/TEVC.2011.2132725","volume":"15","author":"X Chen","year":"2011","unstructured":"Chen X, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evolut Comput 15(5):591\u2013607","journal-title":"IEEE Trans Evolut Comput"},{"issue":"2","key":"9718_CR37","first-page":"115","volume":"9","author":"K Deb","year":"1995","unstructured":"Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9(2):115\u2013148","journal-title":"Complex Syst"},{"issue":"1","key":"9718_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJAISC.2014.059280","volume":"4","author":"K Deb","year":"2014","unstructured":"Deb K, Deb D (2014) Analysing mutation schemes for real-parameter genetic algorithms. Int J Artif Intelli Soft Comput 4(1):1\u201328","journal-title":"Int J Artif Intelli Soft Comput"},{"issue":"14","key":"9718_CR39","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/S0925-2312(01)00612-9","volume":"48","author":"D Liu","year":"2002","unstructured":"Liu D, Hohil ME, Smith SH (2002) N-bit parity neural networks: new solutions based on linear programming. Neurocomputing 48(14):477\u2013488","journal-title":"Neurocomputing"},{"key":"9718_CR40","doi-asserted-by":"crossref","unstructured":"Mangal M, Singh MP (2007) Analysis of pattern classification for the multidimensional parity-bit-checking problem with hybrid evolutionary feed-forward neural network. In: Advances in computational intelligence and learning 14th European symposium on artificial neural networks 2006. Neurocomputing 70(79):1511\u20131524","DOI":"10.1016\/j.neucom.2006.02.022"},{"issue":"22","key":"9718_CR41","first-page":"11125","volume":"218","author":"S Mirjalili","year":"2012","unstructured":"Mirjalili S, Hashim SZM, Sardroudi HM (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218(22):11125\u201311137","journal-title":"Appl Math Comput"},{"issue":"9","key":"9718_CR42","doi-asserted-by":"crossref","first-page":"2924","DOI":"10.1016\/j.asoc.2012.04.010","volume":"12","author":"R Chandra","year":"2012","unstructured":"Chandra R, Frean MR, Zhang M (2012) Crossover-based local search in cooperative co-evolutionary feedforward neural networks. Appl Soft Comput 12(9):2924\u20132932","journal-title":"Appl Soft Comput"},{"key":"9718_CR43","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml\/datasets.html"},{"issue":"5","key":"9718_CR44","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/72.248452","volume":"4","author":"R Reed","year":"1993","unstructured":"Reed R (1993) Pruning algorithms\u2014a survey. IEEE Trans Neural Netw 4(5):740\u2013747","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"9718_CR45","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"5938","key":"9718_CR46","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1126\/science.1175626","volume":"325","author":"AN Meltzoff","year":"2009","unstructured":"Meltzoff AN, Kuhl PK, Movellan J, Sejnowski TJ (2009) Foundations for a new science of learning. Science 325(5938):284\u2013288","journal-title":"Science"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-017-9718-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9718-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9718-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T07:15:04Z","timestamp":1570173304000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-017-9718-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,9]]},"references-count":46,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["9718"],"URL":"https:\/\/doi.org\/10.1007\/s11063-017-9718-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,9]]}}}