{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:56:36Z","timestamp":1773266196020,"version":"3.50.1"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2015,10,9]],"date-time":"2015-10-09T00:00:00Z","timestamp":1444348800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Swarm Intell"],"published-print":{"date-parts":[[2015,12]]},"DOI":"10.1007\/s11721-015-0112-z","type":"journal-article","created":{"date-parts":[[2015,10,9]],"date-time":"2015-10-09T09:38:08Z","timestamp":1444383488000},"page":"229-265","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Learning neural network structures with ant colony algorithms"],"prefix":"10.1007","volume":"9","author":[{"given":"Khalid M.","family":"Salama","sequence":"first","affiliation":[]},{"given":"Ashraf M.","family":"Abdelbar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,10,9]]},"reference":[{"issue":"16\u201318","key":"112_CR1","doi-asserted-by":"crossref","first-page":"3493","DOI":"10.1016\/j.neucom.2007.10.011","volume":"71","author":"J Ang","year":"2008","unstructured":"Ang, J., Tan, K., & Al-Mamun, A. (2008). Training neural networks for classification using growth probability-based evolution. Neurocomputing, 71(16\u201318), 3493\u20133508.","journal-title":"Neurocomputing"},{"issue":"1","key":"112_CR2","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 Transactions on Neural Networks, 5(1), 54\u201365.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"112_CR3","unstructured":"Asuncion, A., Newman, D. (2007). University of California Irvine machine learning repository. http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html ."},{"key":"112_CR4","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer."},{"key":"112_CR5","doi-asserted-by":"crossref","unstructured":"Blum, C., & Socha, K. (2005). Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In Proceedings international conference on hybrid intelligent systems (HIS-2005) (pp. 233\u2013238). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/ICHIS.2005.104"},{"key":"112_CR6","doi-asserted-by":"crossref","unstructured":"Boryczka, U., & Kozak, J. (2010). Ant colony decision trees: A new method for constructing decision trees based on ant colony optimization. In Computational collective intelligence: Technologies and applications (ICCCI-2010), lecture notes in computer science (Vol. 6421, pp. 373\u2013382). Berlin:Springer.","DOI":"10.1007\/978-3-642-16693-8_39"},{"key":"112_CR7","doi-asserted-by":"crossref","unstructured":"Boryczka, U., & Kozak, J. (2011). An adaptive discretization in the ACDT algorithm for continuous attributes. In Computational collective intelligence: Technology and applications (ICCCI-2011), lecture notes in computer science (Vol. 6923, pp. 475\u2013484). Berlin:Springer.","DOI":"10.1007\/978-3-642-23938-0_48"},{"issue":"2","key":"112_CR8","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neunet.2009.11.001","volume":"23","author":"X Cai","year":"2010","unstructured":"Cai, X., Venayagamoorthy, G., & Wunsch, D. (2010). Evolutionary swarm neural network game engine for Capture Go. Neural Networks, 23(2), 295\u2013305.","journal-title":"Neural Networks"},{"key":"112_CR9","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1088\/0954-898X_5_4_005","volume":"5","author":"A Cangelosi","year":"1994","unstructured":"Cangelosi, A., Parisi, D., & Nolfi, S. (1994). Cell division and migration in a \u2018genotype\u2019 for neural networks. Network: Computation in Neural Systems, 5, 497\u2013515.","journal-title":"Network: Computation in Neural Systems"},{"key":"112_CR10","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0925-2312(00)00302-7","volume":"35","author":"P Castillo","year":"2000","unstructured":"Castillo, P., Merelo, J., Prieto, A., Rivas, V., & Romero, G. (2000). G-Prop: Global optimization of multilayer perceptrons using GAs. Neurocomputing, 35, 149\u2013163.","journal-title":"Neurocomputing"},{"issue":"1","key":"112_CR11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TCST.2011.2180386","volume":"21","author":"K Chan","year":"2013","unstructured":"Chan, K., Dillon, T., Chang, E., & Singh, J. (2013). Prediction of short-term traffic variables using intelligent swarm-based neural networks. IEEE Transactions on Control Systems Technology, 21(1), 263\u2013274.","journal-title":"IEEE Transactions on Control Systems Technology"},{"issue":"3","key":"112_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1\u201327.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"112_CR13","doi-asserted-by":"crossref","unstructured":"Clune, J., Beckmann, B., Ofria, C., & Pennock, R. (2009). Evolving coordinated quadruped gaits with the HyperNEAT generative encoding. In Proceedings IEEE congress on evolutionary computation (CEC-2009) (pp. 2764\u20132771). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/CEC.2009.4983289"},{"issue":"4","key":"112_CR14","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.tourman.2008.10.010","volume":"30","author":"J Coshall","year":"2009","unstructured":"Coshall, J. (2009). Combining volatility and smoothing forecasts of UK demand for international tourism. Tourism Management, 30(4), 495\u2013511.","journal-title":"Tourism Management"},{"key":"112_CR15","doi-asserted-by":"crossref","unstructured":"Cussat-Blanc, S., Harrington, K. & Pollack, J. (2015). Gene regulatory network evolution through augmenting topologies. IEEE Transactions on Evolutionary Computation.","DOI":"10.1109\/TEVC.2015.2396199"},{"key":"112_CR16","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.neucom.2004.07.002","volume":"63","author":"Y Da","year":"2005","unstructured":"Da, Y., & Xiurun, G. (2005). An improved PSO-based ANN with simulated annealing technique. Neurocomputing, 63, 527\u2013533.","journal-title":"Neurocomputing"},{"issue":"6","key":"112_CR17","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1016\/j.jss.2012.01.025","volume":"85","author":"S Dehuri","year":"2012","unstructured":"Dehuri, S., Roy, R., Cho, S. B., & Ghosh, A. (2012). An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. Journal of Systems and Software, 85(6), 1333\u20131345.","journal-title":"Journal of Systems and Software"},{"key":"112_CR18","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac, J., Garc\u00eda, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1, 3\u201318.","journal-title":"Swarm and Evolutionary Computation"},{"key":"112_CR19","doi-asserted-by":"crossref","DOI":"10.1007\/b99492","volume-title":"Ant colony optimization","author":"M Dorigo","year":"2004","unstructured":"Dorigo, M., & St\u00fctzle, T. (2004). Ant colony optimization. Cambridge, MA: MIT Press."},{"key":"112_CR20","doi-asserted-by":"crossref","unstructured":"Dorigo, M., & St\u00fctzle, T. (2010). Ant colony optimization: Overview and recent advances. In Handbook of Metaheuristics (pp. 227\u2013263). New York, NY: Springer.","DOI":"10.1007\/978-1-4419-1665-5_8"},{"issue":"1","key":"112_CR21","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/3477.484436","volume":"26","author":"M Dorigo","year":"1996","unstructured":"Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1), 29\u201341.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics"},{"issue":"2","key":"112_CR22","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1162\/106454699568728","volume":"5","author":"M Dorigo","year":"1999","unstructured":"Dorigo, M., Di Caro, G., & Gambardella, L. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137\u2013172.","journal-title":"Artificial Life"},{"issue":"3","key":"112_CR23","first-page":"430","volume":"3","author":"D Dutta","year":"2013","unstructured":"Dutta, D., Roy, A., & Choudhury, K. (2013). Training artificial neural network using particle swarm optimization algorithm. International Journal of Advanced Research in Computer Science and Software Engineering, 3(3), 430\u2013434.","journal-title":"International Journal of Advanced Research in Computer Science and Software Engineering"},{"issue":"2","key":"112_CR24","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/S0954-1810(96)00025-8","volume":"11","author":"J Fang","year":"1997","unstructured":"Fang, J., & Xi, Y. (1997). Neural network design based on evolutionary programming. Artificial Intelligence in Engineering, 11(2), 155\u2013161.","journal-title":"Artificial Intelligence in Engineering"},{"issue":"1","key":"112_CR25","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133\u20133181.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"112_CR26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s12065-007-0002-4","volume":"1","author":"D Floreano","year":"2008","unstructured":"Floreano, D., D\u00fcrr, P., & Mattiussi, C. (2008). Neuroevolution: From architectures to learning. Evolutionary Intelligence, 1(1), 47\u201362.","journal-title":"Evolutionary Intelligence"},{"key":"112_CR27","doi-asserted-by":"crossref","unstructured":"Fogel, D. (1993). Using evolutionary programming to create neural networks that are capable of playing Tic-Tac-Toe. In Proceedings IEEE international conference on neural networks (ICNN-1993) (Vol. 2, pp. 875\u2013880). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/ICNN.1993.298673"},{"key":"112_CR28","doi-asserted-by":"crossref","unstructured":"Galea, M., & Shen, Q. (2006). Simultaneous ant colony optimization algorithms for learning linguistic fuzzy rules. In Swarm intelligence in data mining, studies in computational intelligence (Vol. 34, pp. 75\u201399). Berlin, Heidelberg: Springer.","DOI":"10.1007\/978-3-540-34956-3_4"},{"key":"112_CR29","doi-asserted-by":"crossref","unstructured":"Garro, B., Sossa, H., & Vazquez, R. (2011). Evolving neural networks: A comparison between differential evolution and particle swarm optimization. In Advances in swarm intelligence (ICSI-2011), lecture notes in computer science (Vol. 6728, pp. 447\u2013454). Berlin: Springer.","DOI":"10.1007\/978-3-642-21515-5_53"},{"key":"112_CR30","unstructured":"Goldberg, D., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Proceedings international conference on genetic algorithms (ICGA-1987) (pp. 41\u201349). Hillsdale, NJ: L. Erlbaum Associates."},{"key":"112_CR31","unstructured":"Gomez, F., & Miikkulainen, R. (1999). Solving non-Markovian control tasks with neuroevolution. In Proceedings international joint conference on artificial intelligence (IJCAI-1999) (Vol. 2, pp. 1356\u20131361). San Francisco, CA: Morgan Kaufmann."},{"issue":"2","key":"112_CR32","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1109\/TNN.2010.2093537","volume":"22","author":"P Guti\u00e9rrez","year":"2011","unstructured":"Guti\u00e9rrez, P., Herv\u00e1s-Mart\u00ednez, C., & Mart\u00ednez-Estudillo, F. (2011). Logistic regression by means of evolutionary radial basis function neural networks. IEEE Transactions on Neural Networks, 22(2), 246\u2013263.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"112_CR33","volume-title":"Data mining: Concepts and techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., & Pei, J. (2011a). Data mining: Concepts and techniques. San Francisco, CA: Morgan Kaufmann."},{"issue":"9","key":"112_CR34","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1109\/TNN.2011.2162341","volume":"22","author":"M Han","year":"2011","unstructured":"Han, M., Fan, J., & Wang, J. (2011b). A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control. IEEE Transactions on Neural Networks, 22(9), 1457\u20131468.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"112_CR35","volume-title":"Neural networks and learning machines","author":"S Haykin","year":"2008","unstructured":"Haykin, S. (2008). Neural networks and learning machines. New York, NY: Prentice Hall."},{"issue":"3","key":"112_CR36","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1162\/106454602320991837","volume":"8","author":"G Hornby","year":"2002","unstructured":"Hornby, G., & Pollack, J. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3), 223\u2013246.","journal-title":"Artificial Life"},{"issue":"1","key":"112_CR37","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1023\/A:1022995128597","volume":"17","author":"J Ilonen","year":"2003","unstructured":"Ilonen, J., Kamarainen, J. K., & Lampinen, J. (2003). Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters, 17(1), 93\u2013105.","journal-title":"Neural Processing Letters"},{"key":"112_CR38","volume-title":"Neuro-fuzzy and soft-computing: A computational approach to learning and machine intelligence","author":"JS Jang","year":"1997","unstructured":"Jang, J. S., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft-computing: A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall."},{"issue":"2","key":"112_CR39","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/TSMCB.2003.818557","volume":"34","author":"CF Juang","year":"2004","unstructured":"Juang, C. F. (2004). A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 34(2), 997\u20131006.","journal-title":"IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics"},{"key":"112_CR40","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1016\/j.atmosenv.2010.03.017","volume":"44","author":"D Kang","year":"2010","unstructured":"Kang, D., Mathur, R., & Rao, S. (2010). Real-time bias-adjusted O3 and PM2.5 air quality index forecasts and their performance evaluations over the continental United States. Atmospheric Environment, 44, 2203\u20132212.","journal-title":"Atmospheric Environment"},{"issue":"6","key":"112_CR41","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/91.811231","volume":"7","author":"N Karnik","year":"1999","unstructured":"Karnik, N., Mendel, J., & Liang, Q. (1999). Type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 7(6), 643\u2013658.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"112_CR42","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/095400998116413","volume":"10","author":"J Kodjabachian","year":"1998","unstructured":"Kodjabachian, J., & Meyer, J. A. (1998). Evolution and development of modular control architectures for 1D locomotion in six-legged animats. Connection Science, 10, 211\u2013237.","journal-title":"Connection Science"},{"issue":"1","key":"112_CR43","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/TNN.2002.804317","volume":"14","author":"F Leung","year":"2003","unstructured":"Leung, F., Lam, H., Ling, S., & Tam, P. (2003). Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Transactions on Neural Networks, 14(1), 79\u201388.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"4","key":"112_CR44","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/TEVC.2013.2281531","volume":"18","author":"T Liao","year":"2014","unstructured":"Liao, T., Socha, K., Montes de Oca, M., St\u00fctzle, T., & Dorigo, M. (2014). Ant colony optimization for mixed-variable optimization problems. IEEE Transactions on Evolutionary Computation, 18(4), 503\u2013518.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"112_CR45","doi-asserted-by":"crossref","unstructured":"Lin, C. J., Chen, C. H., & Lin, C. T. (2009). A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, 39(1), 55\u201368.","DOI":"10.1109\/TSMCC.2008.2002333"},{"key":"112_CR46","doi-asserted-by":"crossref","unstructured":"Liu, Y. P., Wu, M. G., & Qian, J. X. (2006). Evolving neural networks using the hybrid of ant colony optimization and BP algorithms. In Advances in neural networks (ISNN-2006), lecture notes in computer science (Vol. 3971, pp. 714\u2013722). Berlin, Heidelberg: Springer.","DOI":"10.1007\/11759966_105"},{"key":"112_CR47","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/S0925-2312(02)00623-9","volume":"51","author":"W Lu","year":"2003","unstructured":"Lu, W., Fan, H., & Lo, S. (2003). Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing, 51, 387\u2013400.","journal-title":"Neurocomputing"},{"issue":"5","key":"112_CR48","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TEVC.2006.890229","volume":"11","author":"D Martens","year":"2007","unstructured":"Martens, D., De Backer, M., Haesen, R., Vanthienen, J., Snoeck, M., & Baesens, B. (2007). Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(5), 651\u2013665.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"112_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-010-5216-5","volume":"82","author":"D Martens","year":"2011","unstructured":"Martens, D., Baesens, B., & Fawcett, T. (2011). Editorial survey: Swarm intelligence for data mining. Machine Learning, 82(1), 1\u201342.","journal-title":"Machine Learning"},{"issue":"3","key":"112_CR50","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/TSMCB.2005.860138","volume":"36","author":"A Mart\u00ednez-Estudillo","year":"2005","unstructured":"Mart\u00ednez-Estudillo, A., Herv\u00e1s-Mart\u00ednez, C., Mart\u00ednez-Estudillo, F., & Garc\u00eda-Pedrajas, N. (2005). Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 36(3), 534\u2013545.","journal-title":"IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics"},{"key":"112_CR51","unstructured":"McDonnel, J., & Waagen, D. (1993). Neural network structure design by evolutionary programming. In Proceedings second annual conference on evolutionary programming (pp. 79\u201389). La Jolla, CA: Evolutionary Programming Society."},{"key":"112_CR52","doi-asserted-by":"crossref","unstructured":"Nawi, N., Khan, A., & Rehman, M. (2013). A new back-propagation neural network optimized with cuckoo search algorithm. In Computational science and its applications (ICCSA-2013), lecture notes in computer science (Vol. 7971, pp. 413\u2013426). Berlin, Heidelberg: Springer.","DOI":"10.1007\/978-3-642-39637-3_33"},{"issue":"3","key":"112_CR53","doi-asserted-by":"crossref","first-page":"181","DOI":"10.12785\/ijcds\/030301","volume":"3","author":"H Okada","year":"2014","unstructured":"Okada, H. (2014). Evolving fuzzy neural networks by particle swarm optimization with fuzzy genotype values. International Journal of Computing and Digital Systems, 3(3), 181\u2013187.","journal-title":"International Journal of Computing and Digital Systems"},{"issue":"11","key":"112_CR54","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1109\/TNN.2011.2169426","volume":"22","author":"T Oong","year":"2011","unstructured":"Oong, T., & Isa, N. (2011). Adaptive evolutionary artificial neural networks for pattern classification. IEEE Transactions on Neural Networks, 22(11), 1823\u20131836.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"112_CR55","doi-asserted-by":"crossref","unstructured":"Otero, F., & Freitas, A. (2013). Improving the interpretability of classification rules discovered by an ant colony algorithm. In Proceedings genetic and evolutionary computation conference (GECCO-2013) (pp. 73\u201380). New York, NY: ACM Press.","DOI":"10.1145\/2463372.2463382"},{"key":"112_CR56","doi-asserted-by":"crossref","unstructured":"Otero, F., Freitas, A., & Johnson, C. (2009). Handling continuous attributes in ant colony classification algorithms. Proceedings IEEE symposium on computational intelligence and data mining (CIDM-2009) (pp. 225\u2013231). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/CIDM.2009.4938653"},{"issue":"11","key":"112_CR57","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1016\/j.asoc.2012.05.028","volume":"12","author":"F Otero","year":"2012","unstructured":"Otero, F., Freitas, A., & Johnson, C. (2012). Inducing decision trees with an ant colony optimization algorithm. Applied Soft Computing, 12(11), 3615\u20133626.","journal-title":"Applied Soft Computing"},{"issue":"1","key":"112_CR58","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/TEVC.2012.2185846","volume":"17","author":"F Otero","year":"2013","unstructured":"Otero, F., Freitas, A., & Johnson, C. (2013). A new sequential covering strategy for inducing classification rules with ant colony algorithms. IEEE Transactions on Evolutionary Computation, 17(1), 64\u201376.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"112_CR59","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1109\/TNN.2005.844858","volume":"16","author":"P Palmes","year":"2005","unstructured":"Palmes, P., Hayasaka, T., & Usui, S. (2005). Mutation-based genetic neural network. IEEE Transactions on Neural Networks, 16(3), 587\u2013600.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"4","key":"112_CR60","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/TEVC.2002.802452","volume":"6","author":"RS Parpinelli","year":"2002","unstructured":"Parpinelli, R. S., Lopes, H. S., & Freitas, A. (2002). Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, 6(4), 321\u2013332.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"112_CR61","unstructured":"Potter, M., & De Jong, K. (1995). Evolving neural networks with collaborative species. In Proceedings summer computer simulation conference (pp. 340\u2013345). Ottawa, Canada: Society for Computer Simulation."},{"key":"112_CR62","unstructured":"Risi, S. & Togelius, J. (2014). Neuroevolution in games: State of the art and open challenges. Tech. Rep. arXiv:1410.7326 , Computing Research Repository (CoRR), http:\/\/arxiv.org\/pdf\/1410.7326 ."},{"key":"112_CR63","doi-asserted-by":"crossref","unstructured":"Salama, K., & Abdelbar, A. (2014). A novel ant colony algorithm for building neural network topologies. In Swarm intelligence (ANTS-2014), lecture notes in computer science (Vol. 8667, pp. 1\u201312). Cham, Switzerland: Springer.","DOI":"10.1007\/978-3-319-09952-1_1"},{"key":"112_CR64","doi-asserted-by":"crossref","unstructured":"Salama, K., & Freitas, A. (2013). Extending the ABC-Miner Bayesian classification algorithm. In Nature inspired cooperative strategies for optimization (NICSO-2013), studies in computational intelligence (Vol. 512, pp. 1\u201312). Cham, Switzerland: Springer.","DOI":"10.1007\/978-3-319-01692-4_1"},{"issue":"2\u20133","key":"112_CR65","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s11721-013-0087-6","volume":"7","author":"K Salama","year":"2013","unstructured":"Salama, K., & Freitas, A. (2013b). Learning Bayesian network classifiers using ant colony optimization. Swarm Intelligence, 7(2\u20133), 229\u2013254.","journal-title":"Swarm Intelligence"},{"issue":"3","key":"112_CR66","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s12293-014-0138-6","volume":"6","author":"K Salama","year":"2014","unstructured":"Salama, K., & Freitas, A. (2014a). ABC-Miner+: Constructing Markov blanket classifiers with ant colony algorithms. Memetic Computing, 6(3), 183\u2013206.","journal-title":"Memetic Computing"},{"key":"112_CR67","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.swevo.2014.05.001","volume":"18","author":"K Salama","year":"2014","unstructured":"Salama, K., & Freitas, A. (2014b). Classification with cluster-based Bayesian multi-nets using ant colony optimization. Swarm and Evolutionary Computation, 18, 54\u201370.","journal-title":"Swarm and Evolutionary Computation"},{"issue":"2","key":"112_CR68","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3233\/IDA-150715","volume":"19","author":"K Salama","year":"2015","unstructured":"Salama, K., & Freitas, A. (2015). Ant colony algorithms for constructing Bayesian multi-net classifiers. Intelligent Data Analysis, 19(2), 233\u2013257.","journal-title":"Intelligent Data Analysis"},{"key":"112_CR69","doi-asserted-by":"crossref","unstructured":"Salama, K., & Otero, F. (2014). Learning multi-tree classification models with ant colony optimization. In Proceedings international conference on evolutionary computation theory and applications (ECTA-14) (pp. 38\u201348). Rome, Italy: Science and Technology Publications.","DOI":"10.5220\/0005071300380048"},{"issue":"3\u20134","key":"112_CR70","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s11721-011-0057-9","volume":"5","author":"K Salama","year":"2011","unstructured":"Salama, K., Abdelbar, A., & Freitas, A. (2011). Multiple pheromone types and other extensions to the ant-miner classification rule discovery algorithm. Swarm Intelligence, 5(3\u20134), 149\u2013182.","journal-title":"Swarm Intelligence"},{"key":"112_CR71","doi-asserted-by":"crossref","unstructured":"Salama, K., Abdelbar, A., Otero, F., & Freitas, A. (2013). Utilizing multiple pheromones in an ant-based algorithm for continuous-attribute classification rule discovery. Applied Soft Computing, 13(1), 667\u2013675.","DOI":"10.1016\/j.asoc.2012.07.026"},{"key":"112_CR72","doi-asserted-by":"crossref","unstructured":"Salerno, J. (1997). Using the particle swarm optimization technique to train a recurrent neural model. In Proceedings IEEE international conference on tools with artificial intelligence (pp. 45\u201349). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/TAI.1997.632235"},{"issue":"3","key":"112_CR73","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/64.393139","volume":"10","author":"N Saravanan","year":"1995","unstructured":"Saravanan, N., & Fogel, D. (1995). Evolving neural control systems. IEEE Expert, 10(3), 23\u201327.","journal-title":"IEEE Expert"},{"issue":"2","key":"112_CR74","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12530-013-9074-9","volume":"4","author":"S Schliebs","year":"2013","unstructured":"Schliebs, S., & Kasabov, N. (2013). Evolving spiking neural network: A survey. Evolving Systems, 4(2), 87\u201398.","journal-title":"Evolving Systems"},{"key":"112_CR75","doi-asserted-by":"crossref","unstructured":"Settles, M., Rodebaugh, B., & Soule, T. (2003). Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network. In Genetic and evolutionary computation (GECCO-2003), lecture notes in computer science (Vol. 2723, pp. 148\u2013149). Berlin, Heidelberg: Springer.","DOI":"10.1007\/3-540-45105-6_17"},{"key":"112_CR76","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s00521-007-0084-z","volume":"16","author":"K Socha","year":"2007","unstructured":"Socha, K., & Blum, C. (2007). An ant colony optimization algorithm for continuous optimization: Application to feed-forward neural network training. Neural Computing & Applications, 16, 235\u2013247.","journal-title":"Neural Computing & Applications"},{"key":"112_CR77","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1016\/j.ejor.2006.06.046","volume":"185","author":"K Socha","year":"2008","unstructured":"Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domains. European Journal of Operational Research, 185, 1155\u20131173.","journal-title":"European Journal of Operational Research"},{"key":"112_CR78","doi-asserted-by":"crossref","first-page":"562","DOI":"10.4236\/jsea.2014.77052","volume":"7","author":"S Sohangir","year":"2014","unstructured":"Sohangir, S., Rahimi, S., & Gupta, B. (2014). Neuroevolutionary feature selection using NEAT. Journal of Software Engineering and Applications, 7, 562\u2013570.","journal-title":"Journal of Software Engineering and Applications"},{"issue":"2","key":"112_CR79","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TNN.2006.890809","volume":"18","author":"Y Song","year":"2007","unstructured":"Song, Y., Chen, Z., & Yuan, Z. (2007). New chaotic PSO-based neural network predictive control for nonlinear process. IEEE Transactions on Neural Networks, 18(2), 595\u2013601.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"2","key":"112_CR80","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10710-007-9028-8","volume":"8","author":"K Stanley","year":"2007","unstructured":"Stanley, K. (2007). Compositional pattern producing networks: A novel abstraction of development. Genetic Progamming and Evolvable Machines, 8(2), 131\u2013162.","journal-title":"Genetic Progamming and Evolvable Machines"},{"key":"112_CR81","unstructured":"Stanley, K. (2015). The neuroevolution of augmenting topologies (NEAT) users page. http:\/\/www.cs.ucf.edu\/~kstanley\/neat.html ."},{"issue":"2","key":"112_CR82","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"K Stanley","year":"2002","unstructured":"Stanley, K., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99\u2013127.","journal-title":"Evolutionary Computation"},{"key":"112_CR83","doi-asserted-by":"crossref","unstructured":"Stanley, K., & Miikkulainen, R. (2004). Evolving a roving eye for Go. In Genetic and evolutionary computation (GECCO-2004), lecture notes in computer science (Vol. 3103, pp. 1226\u20131238). Berlin, Heidelberg: Springer.","DOI":"10.1007\/978-3-540-24855-2_130"},{"key":"112_CR84","unstructured":"Stanley, K., Bryant, B., & Miikkulainen, R. (2005). Evolving neural network agents in the NERO video game. In Proceedings IEEE symposium on computational intelligence and games (CIG-2005) (pp. 182\u2013189). Piscataway, NJ: IEEE Press."},{"issue":"6","key":"112_CR85","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TEVC.2005.856210","volume":"9","author":"K Stanley","year":"2005","unstructured":"Stanley, K., Bryant, B., & Miikkulainen, R. (2005b). Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation, 9(6), 653\u2013668.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"2","key":"112_CR86","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1162\/artl.2009.15.2.15202","volume":"15","author":"K Stanley","year":"2009","unstructured":"Stanley, K., D\u2019Ambrosio, D., & Gauci, J. (2009). A hybercube-based encoding for evolving large-scale neural networks. Artificial Life, 15(2), 185\u2013212.","journal-title":"Artificial Life"},{"key":"112_CR87","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/S0167-739X(00)00043-1","volume":"16","author":"T St\u00fctzle","year":"2000","unstructured":"St\u00fctzle, T., & Hoos, H. (2000). MAX\u2013MIN ant system. Future Generation Computer Systems, 16, 889\u2013914.","journal-title":"Future Generation Computer Systems"},{"key":"112_CR88","volume-title":"Introduction to data mining","author":"PN Tan","year":"2005","unstructured":"Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Boston, MA: Addison Wesley."},{"issue":"3","key":"112_CR89","doi-asserted-by":"crossref","first-page":"36","DOI":"10.5121\/ijaia.2011.2304","volume":"2","author":"E Valian","year":"2011","unstructured":"Valian, E., Mohanna, S., & Tavakoli, S. (2011). Improved cuckoo search algorithm for feedforward neural network training. International Journal of Artificial Intelligence and Applications, 2(3), 36\u201343.","journal-title":"International Journal of Artificial Intelligence and Applications"},{"issue":"3","key":"112_CR90","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/TEVC.2011.2112663","volume":"15","author":"VK Valsalam","year":"2011","unstructured":"Valsalam, V. K., & Miikkulainen, R. (2011). Evolving symmetry for modular system design. IEEE Transactions on Evolutionary Computation, 15(3), 368\u2013386.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"112_CR91","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12065-011-0067-y","volume":"5","author":"VK Valsalam","year":"2012","unstructured":"Valsalam, V. K., Hiller, J., MacCurdy, R., Lipson, H., & Miikkulainen, R. (2012). Constructing controllers for physical multilegged robots using the ENSO neuroevolution approach. Evolutionary Intelligence, 5(1), 45\u201356.","journal-title":"Evolutionary Intelligence"},{"key":"112_CR92","volume-title":"The roots of backpropagation: From ordered derivatives to neural networks and political forecasting","author":"PJ Werbos","year":"1994","unstructured":"Werbos, P. J. (1994). The roots of backpropagation: From ordered derivatives to neural networks and political forecasting. New York, NY: Wiley-Interscience."},{"key":"112_CR93","doi-asserted-by":"crossref","unstructured":"Whiteson, S., Stone, P., Stanley, K., Miikkulainen, R., & Kohl, N. (2005). Automatic feature selection in neuroevolution. In Proceedings genetic and evolutionary computation conference (GECCO-2005) (pp. 1225\u20131232). New York, NY: ACM Press.","DOI":"10.1145\/1068009.1068210"},{"issue":"3","key":"112_CR94","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/0167-8191(90)90086-O","volume":"14","author":"D Whitley","year":"1990","unstructured":"Whitley, D., Starkweather, T., & Bogart, C. (1990). Genetic algorithms and neural networks: Optimizing connections and connectivity. Parallel Computing, 14(3), 347\u2013361.","journal-title":"Parallel Computing"},{"issue":"2\u20133","key":"112_CR95","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1023\/A:1022674030396","volume":"13","author":"D Whitley","year":"1993","unstructured":"Whitley, D., Dominic, S., Das, R., & Anderson, C. (1993). Genetic reinforcement learning for neurocontrol problems. Machine Learning, 13(2\u20133), 259\u2013284.","journal-title":"Machine Learning"},{"key":"112_CR96","volume-title":"Data mining: Practical machine learning tools and techniques","author":"IH Witten","year":"2010","unstructured":"Witten, I. H., Frank, E., & Hall, M. A. (2010). Data mining: Practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann."},{"key":"112_CR97","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1007\/s521-001-8050-2","volume":"10","author":"JM Yang","year":"2001","unstructured":"Yang, J. M., & Kao, C. Y. (2001). A robust evolutionary algorithm for training neural networks. Neural Computing and Applications, 10, 214\u2013230.","journal-title":"Neural Computing and Applications"},{"issue":"3","key":"112_CR98","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/72.572107","volume":"8","author":"X Yao","year":"1997","unstructured":"Yao, X., & Liu, Y. (1997). A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8(3), 694\u2013713.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"12","key":"112_CR99","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1109\/TNN.2011.2170095","volume":"22","author":"CY Yeh","year":"2011","unstructured":"Yeh, C. Y., Jeng, W. R., & Lee, S. J. (2011). Data-based system modeling using a type-2 fuzzy neural network with a hybrid learning algorithm. IEEE Transactions on Neural Networks, 22(12), 2296\u20132309.","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"4","key":"112_CR100","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TNNLS.2012.2232678","volume":"24","author":"WC Yeh","year":"2013","unstructured":"Yeh, W. C. (2013). New parameter-free simplified swarm optimization for artificial neural network training and its application in the prediction of time series. IEEE Transactions on Neural Networks and Learning Systems, 24(4), 661\u2013665.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"112_CR101","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s11063-007-9053-x","volume":"26","author":"J Yu","year":"2007","unstructured":"Yu, J., Xi, L., & Wang, S. (2007). An improved particle swarm optimization for evolving feedforward artificial neural networks. Neural Processing Letters, 26(3), 217\u2013231.","journal-title":"Neural Processing Letters"},{"issue":"4","key":"112_CR102","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1016\/j.neucom.2007.10.013","volume":"71","author":"J Yu","year":"2008","unstructured":"Yu, J., Wang, S., & Xi, L. (2008). Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing, 71(4), 1054\u20131060.","journal-title":"Neurocomputing"}],"container-title":["Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-015-0112-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11721-015-0112-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11721-015-0112-z","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T10:19:41Z","timestamp":1567246781000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11721-015-0112-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10,9]]},"references-count":102,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,12]]}},"alternative-id":["112"],"URL":"https:\/\/doi.org\/10.1007\/s11721-015-0112-z","relation":{},"ISSN":["1935-3812","1935-3820"],"issn-type":[{"value":"1935-3812","type":"print"},{"value":"1935-3820","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,10,9]]}}}