{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:29:04Z","timestamp":1772303344865,"version":"3.50.1"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Tokushima University Tenure-Track Faculty Development Support System, Tokushima University, Japan."},{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["19H01096"],"award-info":[{"award-number":["19H01096"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genet Program Evolvable Mach"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s10710-025-09517-6","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T06:08:57Z","timestamp":1749017337000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Chaotic map-coded metaheuristics for metameric variable-length problems"],"prefix":"10.1007","volume":"26","author":[{"given":"Haichuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haotian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoya","family":"Chiba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shingo","family":"Kagami","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koichi","family":"Hashimoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuichi","family":"Nagata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"issue":"6","key":"9517_CR1","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1134\/S0040579517060057","volume":"51","author":"I Grossmann","year":"2017","unstructured":"I. Grossmann, R. Apap, B. Calfa, P. Garcia-Herreros, Q. Zhang, Mathematical programming techniques for optimization under uncertainty and their application in process systems engineering. Theor. Found. Chem. Eng. 51(6), 893\u2013909 (2017). https:\/\/doi.org\/10.1134\/S0040579517060057","journal-title":"Theor. Found. Chem. Eng."},{"issue":"4","key":"9517_CR2","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1007\/s10462-017-9605-z","volume":"52","author":"K Hussain","year":"2019","unstructured":"K. Hussain, M.N.M. Salleh, S. Cheng, Y. Shi, Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 52(4), 2191\u20132233 (2019). https:\/\/doi.org\/10.1007\/s10462-017-9605-z","journal-title":"Artif. Intell. Rev."},{"key":"9517_CR3","doi-asserted-by":"publisher","unstructured":"M.R. Garey, D.S. Johnson, L. Stockmeyer, Some simplified np-complete problems, in Proceedings of the Sixth Annual ACM Symposium on Theory of Computing (1974), pp. 47\u201363. https:\/\/doi.org\/10.1145\/800119.803884","DOI":"10.1145\/800119.803884"},{"issue":"1","key":"9517_CR4","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"J.H. Holland, Genetic algorithms. Sci. Am. 267(1), 66\u201373 (1992)","journal-title":"Sci. Am."},{"issue":"4","key":"9517_CR5","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"R. Storn, K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997). https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"J. Glob. Optim."},{"key":"9517_CR6","doi-asserted-by":"publisher","unstructured":"R. Tanabe, A. Fukunaga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (2013), pp. 71\u201378. https:\/\/doi.org\/10.1109\/CEC.2013.6557555","DOI":"10.1109\/CEC.2013.6557555"},{"issue":"1","key":"9517_CR7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2011","unstructured":"S. Das, P.N. Suganthan, Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4\u201331 (2011). https:\/\/doi.org\/10.1109\/TEVC.2010.2059031","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9517_CR8","doi-asserted-by":"publisher","unstructured":"R. Tanabe, A.S. Fukunaga, Improving the search performance of shade using linear population size reduction, in 2014 IEEE Congress on Evolutionary Computation (CEC) (2014), pp. 1658\u20131665. https:\/\/doi.org\/10.1109\/CEC.2014.6900380","DOI":"10.1109\/CEC.2014.6900380"},{"issue":"4","key":"9517_CR9","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"M. Dorigo, M. Birattari, T. Stutzle, Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006). https:\/\/doi.org\/10.1109\/MCI.2006.329691","journal-title":"IEEE Comput. Intell. Mag."},{"key":"9517_CR10","doi-asserted-by":"publisher","unstructured":"J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of ICNN\u201995\u2014International Conference on Neural Networks, vol. 4 (1995), pp. 1942\u201319484. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"10","key":"9517_CR11","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1109\/TCYB.2015.2475174","volume":"46","author":"Y-J Gong","year":"2016","unstructured":"Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H.S.-H. Chung, Y.-H. Shi, J. Zhang, Genetic learning particle swarm optimization. IEEE Trans. Cybern. 46(10), 2277\u20132290 (2016). https:\/\/doi.org\/10.1109\/TCYB.2015.2475174","journal-title":"IEEE Trans. Cybern."},{"key":"9517_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109081","author":"H Yang","year":"2022","unstructured":"H. Yang, Y. Yu, J. Cheng, Z. Lei, Z. Cai, Z. Zhang, S. Gao, An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration. Knowl. Based Syst. (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109081","journal-title":"Knowl. Based Syst."},{"issue":"11","key":"9517_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-024-00988-7","volume":"18","author":"H Escobar-Cuevas","year":"2025","unstructured":"H. Escobar-Cuevas, E. Cuevas, J. Lopez, M. Perez-Cisneros, Integration of metaheuristic operators through unstructured evolutive game theory approach: a novel hybrid methodology. Evol. Intell. 18(11), 1\u201328 (2025). https:\/\/doi.org\/10.1007\/s12065-024-00988-7","journal-title":"Evol. Intell."},{"key":"9517_CR14","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.matcom.2024.08.004","volume":"227","author":"E Cuevas","year":"2025","unstructured":"E. Cuevas, M. V\u00e1squez, K. Avila, A. Rodriguez, D. Zaldivar, Balancing individual and collective strategies: a new approach in metaheuristic optimization. Math. Comput. Simul. 227, 322\u2013346 (2025). https:\/\/doi.org\/10.1016\/j.matcom.2024.08.004","journal-title":"Math. Comput. Simul."},{"key":"9517_CR15","doi-asserted-by":"publisher","first-page":"22119","DOI":"10.1007\/s00521-024-10372-2","volume":"36","author":"BA Rivera-Aguilar","year":"2024","unstructured":"B.A. Rivera-Aguilar, E. Cuevas, D. Zald\u00edvar, M.A. P\u00e9rez-Cisneros, A metaheuristic algorithm based on a radial basis function neural networks. Neural Comput. Appl. 36, 22119\u201322147 (2024). https:\/\/doi.org\/10.1007\/s00521-024-10372-2","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"9517_CR16","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s10710-016-9282-8","volume":"18","author":"ML Ryerkerk","year":"2017","unstructured":"M.L. Ryerkerk, R.C. Averill, K. Deb, E.D. Goodman, Solving metameric variable-length optimization problems using genetic algorithms. Genet. Program. Evolvable Mach. 18(2), 247\u2013277 (2017). https:\/\/doi.org\/10.1007\/s10710-016-9282-8","journal-title":"Genet. Program. Evolvable Mach."},{"issue":"4","key":"9517_CR17","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TSMCB.2008.2010951","volume":"39","author":"C-K Ting","year":"2009","unstructured":"C.-K. Ting, C.-N. Lee, H.-C. Chang, J.-S. Wu, Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(4), 945\u2013958 (2009). https:\/\/doi.org\/10.1109\/TSMCB.2008.2010951","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"issue":"7","key":"9517_CR18","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.1016\/j.renene.2010.10.034","volume":"36","author":"JS Gonz\u00e1lez","year":"2011","unstructured":"J.S. Gonz\u00e1lez, A.G. Rodr\u00edguez, J.C. Mora, M.B. Pay\u00e1n, J.R. Santos, Overall design optimization of wind farms. Renew. Energy 36(7), 1973\u20131982 (2011). https:\/\/doi.org\/10.1016\/j.renene.2010.10.034","journal-title":"Renew. Energy"},{"issue":"2","key":"9517_CR19","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TNNLS.2018.2846646","volume":"30","author":"S Gao","year":"2019","unstructured":"S. Gao, M. Zhou, Y. Wang, J. Cheng, H. Yachi, J. Wang, Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans. Neural Netw. Learn. Syst. 30(2), 601\u2013614 (2019). https:\/\/doi.org\/10.1109\/TNNLS.2018.2846646","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"9517_CR20","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1046\/j.1525-142X.2003.03027.x","volume":"5","author":"C Kettle","year":"2003","unstructured":"C. Kettle, J. Johnstone, T. Jowett, H. Arthur, W. Arthur, The pattern of segment formation, as revealed by engrailed expression, in a centipede with a variable number of segments. Evol. Dev. 5(2), 198\u2013207 (2003). https:\/\/doi.org\/10.1046\/j.1525-142X.2003.03027.x","journal-title":"Evol. Dev."},{"issue":"4","key":"9517_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s10710-019-09356-2","volume":"20","author":"M Ryerkerk","year":"2019","unstructured":"M. Ryerkerk, R. Averill, K. Deb, E. Goodman, A survey of evolutionary algorithms using metameric representations. Genet. Program. Evolvable Mach. 20(4), 441\u2013478 (2019). https:\/\/doi.org\/10.1007\/s10710-019-09356-2","journal-title":"Genet. Program. Evolvable Mach."},{"key":"9517_CR22","doi-asserted-by":"publisher","unstructured":"F. Rothlauf, Representations for Genetic and Evolutionary Algorithms (Springer, Berlin, Heidelberg, 2006), pp. 9\u201332. https:\/\/doi.org\/10.1007\/3-540-32444-5_2","DOI":"10.1007\/3-540-32444-5_2"},{"key":"9517_CR23","doi-asserted-by":"publisher","first-page":"112306","DOI":"10.1016\/j.engstruct.2021.112306","volume":"240","author":"M Khodzhaiev","year":"2021","unstructured":"M. Khodzhaiev, U. Reuter, Structural optimization of transmission towers using a novel genetic algorithm approach with a variable length genome. Eng. Struct. 240, 112306 (2021). https:\/\/doi.org\/10.1016\/j.engstruct.2021.112306","journal-title":"Eng. Struct."},{"key":"9517_CR24","doi-asserted-by":"publisher","first-page":"115732","DOI":"10.1016\/j.eswa.2021.115732","volume":"186","author":"R Pal","year":"2021","unstructured":"R. Pal, T.D. Chaudhuri, S. Mukhopadhyay, Portfolio formation and optimization with continuous realignment: a suggested method for choosing the best portfolio of stocks using variable length NSGA-II. Expert Syst. Appl. 186, 115732 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.115732","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"9517_CR25","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.bbe.2021.02.002","volume":"41","author":"M-A Tsompanas","year":"2021","unstructured":"M.-A. Tsompanas, L. Bull, A. Adamatzky, I. Balaz, Metameric representations on optimization of nano particle cancer treatment. Biocybern. Biomed. Eng. 41(2), 352\u2013361 (2021). https:\/\/doi.org\/10.1016\/j.bbe.2021.02.002","journal-title":"Biocybern. Biomed. Eng."},{"key":"9517_CR26","doi-asserted-by":"publisher","first-page":"108902","DOI":"10.1016\/j.asoc.2022.108902","volume":"123","author":"Y Jiang","year":"2022","unstructured":"Y. Jiang, L. Tang, H. Liu, A. Zeng, A variable-length encoding genetic algorithm for incremental service composition in uncertain environments for cloud manufacturing. Appl. Soft Comput. 123, 108902 (2022). https:\/\/doi.org\/10.1016\/j.asoc.2022.108902","journal-title":"Appl. Soft Comput."},{"issue":"11","key":"9517_CR27","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1016\/j.asoc.2012.06.019","volume":"12","author":"Z Su","year":"2012","unstructured":"Z. Su, P. Wang, J. Shen, Y. Li, Y. Zhang, E. Hu, Automatic fuzzy partitioning approach using variable string length artificial bee colony (VABC) algorithm. Appl. Soft Comput. 12(11), 3421\u20133441 (2012). https:\/\/doi.org\/10.1016\/j.asoc.2012.06.019","journal-title":"Appl. Soft Comput."},{"key":"9517_CR28","doi-asserted-by":"publisher","first-page":"107529","DOI":"10.1016\/j.asoc.2021.107529","volume":"109","author":"A Mohammadi","year":"2021","unstructured":"A. Mohammadi, S.H. Zahiri, S.M. Razavi, P.N. Suganthan, Design and modeling of adaptive IIR filtering systems using a weighted sum-variable length particle swarm optimization. Appl. Soft Comput. 109, 107529 (2021). https:\/\/doi.org\/10.1016\/j.asoc.2021.107529","journal-title":"Appl. Soft Comput."},{"key":"9517_CR29","doi-asserted-by":"publisher","unstructured":"J. Huang, B. Xue, Y. Sun, M. Zhang, A flexible variable-length particle swarm optimization approach to convolutional neural network architecture design, in 2021 IEEE Congress on Evolutionary Computation (CEC) (2021), pp. 934\u2013941. https:\/\/doi.org\/10.1109\/CEC45853.2021.9504716","DOI":"10.1109\/CEC45853.2021.9504716"},{"issue":"1","key":"9517_CR30","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ejor.2014.09.025","volume":"242","author":"Y Chen","year":"2015","unstructured":"Y. Chen, V. Mahalec, Y. Chen, X. Liu, R. He, K. Sun, Reconfiguration of satellite orbit for cooperative observation using variable-size multi-objective differential evolution. Eur. J. Oper. Res. 242(1), 10\u201320 (2015). https:\/\/doi.org\/10.1016\/j.ejor.2014.09.025","journal-title":"Eur. J. Oper. Res."},{"issue":"9","key":"9517_CR31","doi-asserted-by":"publisher","first-page":"100567","DOI":"10.1016\/j.patter.2022.100567","volume":"3","author":"A Ghosh","year":"2022","unstructured":"A. Ghosh, N.D. Jana, S. Mallik, Z. Zhao, Designing optimal convolutional neural network architecture using differential evolution algorithm. Patterns 3(9), 100567 (2022)","journal-title":"Patterns"},{"issue":"3","key":"9517_CR32","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1109\/TEVC.2018.2869405","volume":"23","author":"B Tran","year":"2019","unstructured":"B. Tran, B. Xue, M. Zhang, Variable-length particle swarm optimization for feature selection on high-dimensional classification. IEEE Trans. Evol. Comput. 23(3), 473\u2013487 (2019). https:\/\/doi.org\/10.1109\/TEVC.2018.2869405","journal-title":"IEEE Trans. Evol. Comput."},{"key":"9517_CR33","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10710-007-9028-8","volume":"8","author":"KO Stanley","year":"2007","unstructured":"K.O. Stanley, Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8, 131\u2013162 (2007). https:\/\/doi.org\/10.1007\/s10710-007-9028-8","journal-title":"Genet. Program. Evolvable Mach."},{"issue":"1","key":"9517_CR34","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s42256-018-0006-z","volume":"1","author":"KO Stanley","year":"2019","unstructured":"K.O. Stanley, J. Clune, J. Lehman, R. Miikkulainen, Designing neural networks through neuroevolution. Nat. Mach. Intell. 1(1), 24\u201335 (2019). https:\/\/doi.org\/10.1038\/s42256-018-0006-z","journal-title":"Nat. Mach. Intell."},{"issue":"5258","key":"9517_CR35","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1038\/227561a0","volume":"227","author":"F Crick","year":"1970","unstructured":"F. Crick, Central dogma of molecular biology. Nature 227(5258), 561\u2013563 (1970). https:\/\/doi.org\/10.1038\/227561a0","journal-title":"Nature"},{"issue":"3","key":"9517_CR36","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s10441-008-9047-8","volume":"56","author":"M Bizzarri","year":"2008","unstructured":"M. Bizzarri, A. Cucina, F. Conti, F. D\u2019Anselmi, Beyond the oncogene paradigm: understanding complexity in cancerogenesis. Acta. Biotheor. 56(3), 173\u2013196 (2008). https:\/\/doi.org\/10.1007\/s10441-008-9047-8","journal-title":"Acta. Biotheor."},{"issue":"6","key":"9517_CR37","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1038\/nbt0694-596","volume":"12","author":"JE Skinner","year":"1994","unstructured":"J.E. Skinner, Low-dimensional chaos in biological systems. Bio\/technology 12(6), 596\u2013600 (1994). https:\/\/doi.org\/10.1038\/nbt0694-596","journal-title":"Bio\/technology"},{"issue":"1","key":"9517_CR38","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/s41467-018-07932-1","volume":"10","author":"ML Heltberg","year":"2019","unstructured":"M.L. Heltberg, S. Krishna, M.H. Jensen, On chaotic dynamics in transcription factors and the associated effects in differential gene regulation. Nat. Commun. 10(1), 71 (2019). https:\/\/doi.org\/10.1038\/s41467-018-07932-1","journal-title":"Nat. Commun."},{"issue":"6","key":"9517_CR39","doi-asserted-by":"publisher","first-page":"3954","DOI":"10.1109\/TSMC.2019.2956121","volume":"51","author":"S Gao","year":"2021","unstructured":"S. Gao, Y. Yu, Y. Wang, J. Wang, J. Cheng, M. Zhou, Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans. Syst. Man Cybern.: Syst. 51(6), 3954\u20133967 (2021). https:\/\/doi.org\/10.1109\/TSMC.2019.2956121","journal-title":"IEEE Trans. Syst. Man Cybern.: Syst."},{"key":"9517_CR40","doi-asserted-by":"publisher","unstructured":"H. Yang, Y. Yang, Y. Zhang, C. Tang, K. Hashimoto, Y. Nagata, Chaotic map-coded evolutionary algorithms for dendritic neuron model optimization, in 2024 IEEE Congress on Evolutionary Computation (CEC) (2024), pp. 1\u20138. https:\/\/doi.org\/10.1109\/CEC60901.2024.10612087","DOI":"10.1109\/CEC60901.2024.10612087"},{"issue":"4","key":"9517_CR41","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"W.S. McCulloch, W. Pitts, A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115\u2013133 (1943). https:\/\/doi.org\/10.1007\/BF02478259","journal-title":"Bull. Math. Biophys."},{"issue":"12","key":"9517_CR42","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2022","unstructured":"Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"7","key":"9517_CR43","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Y. Yu, X. Si, C. Hu, J. Zhang, A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput. 31(7), 1235\u20131270 (2019). https:\/\/doi.org\/10.1162\/neco_a_01199","journal-title":"Neural Comput."},{"key":"9517_CR44","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., 2017)"},{"key":"9517_CR45","unstructured":"J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, Bert: pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805"},{"key":"9517_CR46","unstructured":"T.B. Brown, Language models are few-shot learners (2020). arXiv:2005.14165"},{"issue":"6","key":"9517_CR47","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"E.M. Izhikevich, Simple model of spiking neurons. IEEE Trans. Neural Netw. 14(6), 1569\u20131572 (2003). https:\/\/doi.org\/10.1109\/TNN.2003.820440","journal-title":"IEEE Trans. Neural Netw."},{"issue":"11","key":"9517_CR48","doi-asserted-by":"publisher","first-page":"2709","DOI":"10.1162\/089976602760408035","volume":"14","author":"J \u0160\u00edma","year":"2002","unstructured":"J. \u0160\u00edma, Training a single sigmoidal neuron is hard. Neural Comput. 14(11), 2709\u20132728 (2002). https:\/\/doi.org\/10.1162\/089976602760408035","journal-title":"Neural Comput."},{"issue":"5","key":"9517_CR49","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.3390\/math11051251","volume":"11","author":"Y Zhang","year":"2023","unstructured":"Y. Zhang, Y. Yang, X. Li, Z. Yuan, Y. Todo, H. Yang, A dendritic neuron model optimized by meta-heuristics with a power-law-distributed population interaction network for financial time-series forecasting. Mathematics 11(5), 1251 (2023). https:\/\/doi.org\/10.3390\/math11051251","journal-title":"Mathematics"},{"issue":"1","key":"9517_CR50","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/JAS.2021.1004284","volume":"9","author":"Y Yu","year":"2022","unstructured":"Y. Yu, Z. Lei, Y. Wang, T. Zhang, C. Peng, S. Gao, Improving dendritic neuron model with dynamic scale-free network-based differential evolution. IEEE\/CAA J. Autom. Sin. 9(1), 99\u2013110 (2022). https:\/\/doi.org\/10.1109\/JAS.2021.1004284","journal-title":"IEEE\/CAA J. Autom. Sin."},{"issue":"11","key":"9517_CR51","doi-asserted-by":"publisher","first-page":"1051","DOI":"10.3390\/axioms12111051","volume":"12","author":"H Yang","year":"2023","unstructured":"H. Yang, Y. Zhang, C. Zhang, W. Xia, Y. Yang, Z. Zhang, A hyperparameter self-evolving shade-based dendritic neuron model for classification. Axioms 12(11), 1051 (2023). https:\/\/doi.org\/10.3390\/axioms12111051","journal-title":"Axioms"},{"key":"9517_CR52","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.enconman.2014.08.037","volume":"88","author":"M Cheng","year":"2014","unstructured":"M. Cheng, Y. Zhu, The state of the art of wind energy conversion systems and technologies: a review. Energy Convers. Manag. 88, 332\u2013347 (2014). https:\/\/doi.org\/10.1016\/j.enconman.2014.08.037","journal-title":"Energy Convers. Manag."},{"key":"9517_CR53","doi-asserted-by":"publisher","first-page":"125779","DOI":"10.1016\/j.jclepro.2020.125779","volume":"289","author":"P Sadorsky","year":"2021","unstructured":"P. Sadorsky, Wind energy for sustainable development: driving factors and future outlook. J. Clean. Prod. 289, 125779 (2021). https:\/\/doi.org\/10.1016\/j.jclepro.2020.125779","journal-title":"J. Clean. Prod."},{"key":"9517_CR54","doi-asserted-by":"publisher","first-page":"102680","DOI":"10.1016\/j.resourpol.2022.102680","volume":"77","author":"H Chen","year":"2022","unstructured":"H. Chen, Y. Shi, X. Zhao, Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: a case of us economy. Resour. Policy 77, 102680 (2022). https:\/\/doi.org\/10.1016\/j.resourpol.2022.102680","journal-title":"Resour. Policy"},{"key":"9517_CR55","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1016\/j.apenergy.2018.07.084","volume":"228","author":"AP Marug\u00e1n","year":"2018","unstructured":"A.P. Marug\u00e1n, F.P.G. M\u00e1rquez, J.M.P. Perez, D. Ruiz-Hern\u00e1ndez, A survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822\u20131836 (2018). https:\/\/doi.org\/10.1016\/j.apenergy.2018.07.084","journal-title":"Appl. Energy"},{"key":"9517_CR56","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.renene.2021.10.02","volume":"182","author":"R McKenna","year":"2022","unstructured":"R. McKenna, S. Pfenninger, H. Heinrichs, J. Schmidt, I. Staffell, C. Bauer, K. Gruber, A.N. Hahmann, M. Jansen, M. Klingler et al., High-resolution large-scale onshore wind energy assessments: a review of potential definitions, methodologies and future research needs. Renew. Energy 182, 659\u2013684 (2022). https:\/\/doi.org\/10.1016\/j.renene.2021.10.02","journal-title":"Renew. Energy"},{"key":"9517_CR57","doi-asserted-by":"publisher","first-page":"115090","DOI":"10.1016\/j.apenergy.2020.115090","volume":"269","author":"SR Reddy","year":"2020","unstructured":"S.R. Reddy, Wind farm layout optimization (WindFLO): an advanced framework for fast wind farm analysis and optimization. Appl. Energy 269, 115090 (2020). https:\/\/doi.org\/10.1016\/j.apenergy.2020.115090","journal-title":"Appl. Energy"},{"key":"9517_CR58","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.renene.2017.02.017","volume":"107","author":"L Parada","year":"2017","unstructured":"L. Parada, C. Herrera, P. Flores, V. Parada, Wind farm layout optimization using a Gaussian-based wake model. Renew. Energy 107, 531\u2013541 (2017). https:\/\/doi.org\/10.1016\/j.renene.2017.02.017","journal-title":"Renew. Energy"},{"issue":"8","key":"9517_CR59","doi-asserted-by":"publisher","first-page":"081002","DOI":"10.1115\/1.4006997","volume":"134","author":"BL Du Pont","year":"2012","unstructured":"B.L. Du Pont, J. Cagan, An extended pattern search approach to wind farm layout optimization. J. Mech. Des. 134(8), 081002 (2012). https:\/\/doi.org\/10.1115\/1.4006997","journal-title":"J. Mech. Des."},{"key":"9517_CR60","doi-asserted-by":"publisher","first-page":"115047","DOI":"10.1016\/j.enconman.2021.115047","volume":"252","author":"F Bai","year":"2022","unstructured":"F. Bai, X. Ju, S. Wang, W. Zhou, F. Liu, Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo tree search reinforcement learning. Energy Convers. Manag. 252, 115047 (2022). https:\/\/doi.org\/10.1016\/j.enconman.2021.115047","journal-title":"Energy Convers. Manag."},{"issue":"1","key":"9517_CR61","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/0167-6105(94)90080-9","volume":"51","author":"G Mosetti","year":"1994","unstructured":"G. Mosetti, C. Poloni, B. Diviacco, Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J. Wind Eng. Ind. Aerodyn. 51(1), 105\u2013116 (1994). https:\/\/doi.org\/10.1016\/0167-6105(94)90080-9","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"issue":"2","key":"9517_CR62","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.renene.2004.05.007","volume":"30","author":"S Grady","year":"2005","unstructured":"S. Grady, M. Hussaini, M.M. Abdullah, Placement of wind turbines using genetic algorithms. Renew. Energy 30(2), 259\u2013270 (2005). https:\/\/doi.org\/10.1016\/j.renene.2004.05.007","journal-title":"Renew. Energy"},{"key":"9517_CR63","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.jweia.2015.01.018","volume":"139","author":"X Gao","year":"2015","unstructured":"X. Gao, H. Yang, L. Lin, P. Koo, Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore. J. Wind Eng. Ind. Aerodyn. 139, 89\u201399 (2015). https:\/\/doi.org\/10.1016\/j.jweia.2015.01.018","journal-title":"J. Wind Eng. Ind. Aerodyn."},{"key":"9517_CR64","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.renene.2018.02.083","volume":"123","author":"AM Abdelsalam","year":"2018","unstructured":"A.M. Abdelsalam, M. El-Shorbagy, Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renew. Energy 123, 748\u2013755 (2018). https:\/\/doi.org\/10.1016\/j.renene.2018.02.083","journal-title":"Renew. Energy"},{"key":"9517_CR65","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1016\/j.rser.2015.12.229","volume":"58","author":"R Shakoor","year":"2016","unstructured":"R. Shakoor, M.Y. Hassan, A. Raheem, Y.-K. Wu, Wake effect modeling: a review of wind farm layout optimization using Jensen\u2019s model. Renew. Sustain. Energy Rev. 58, 1048\u20131059 (2016). https:\/\/doi.org\/10.1016\/j.rser.2015.12.229","journal-title":"Renew. Sustain. Energy Rev."},{"key":"9517_CR66","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.renene.2012.04.052","volume":"48","author":"C Wan","year":"2012","unstructured":"C. Wan, J. Wang, G. Yang, H. Gu, X. Zhang, Wind farm micro-siting by gaussian particle swarm optimization with local search strategy. Renew. Energy 48, 276\u2013286 (2012). https:\/\/doi.org\/10.1016\/j.renene.2012.04.052","journal-title":"Renew. Energy"},{"key":"9517_CR67","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.renene.2011.12.013","volume":"44","author":"Y Ero\u011flu","year":"2012","unstructured":"Y. Ero\u011flu, S.U. Se\u00e7kiner, Design of wind farm layout using ant colony algorithm. Renew. Energy 44, 53\u201362 (2012). https:\/\/doi.org\/10.1016\/j.renene.2011.12.013","journal-title":"Renew. Energy"},{"issue":"15","key":"9517_CR68","doi-asserted-by":"publisher","first-page":"6585","DOI":"10.1016\/j.eswa.2014.04.044","volume":"41","author":"FG Montoya","year":"2014","unstructured":"F.G. Montoya, F. Manzano-Agugliaro, S. L\u00f3pez-M\u00e1rquez, Q. Hern\u00e1ndez-Escobedo, C. Gil, Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms. Expert Syst. Appl. 41(15), 6585\u20136595 (2014). https:\/\/doi.org\/10.1016\/j.eswa.2014.04.044","journal-title":"Expert Syst. Appl."},{"key":"9517_CR69","doi-asserted-by":"publisher","first-page":"106198","DOI":"10.1016\/j.engappai.2023.106198","volume":"123","author":"H Yang","year":"2023","unstructured":"H. Yang, S. Gao, Z. Lei, J. Li, Y. Yu, Y. Wang, An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem. Eng. Appl. Artif. Intell. 123, 106198 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.106198","journal-title":"Eng. Appl. Artif. Intell."},{"key":"9517_CR70","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/s11831-021-09586-7","volume":"29","author":"T Kunakote","year":"2022","unstructured":"T. Kunakote, N. Sabangban, S. Kumar, G.G. Tejani, N. Panagant, N. Pholdee, S. Bureerat, A.R. Yildiz, Comparative performance of twelve metaheuristics for wind farm layout optimisation. Arch. Comput. Methods Eng. 29, 717\u2013730 (2022). https:\/\/doi.org\/10.1007\/s11831-021-09586-7","journal-title":"Arch. Comput. Methods Eng."},{"key":"9517_CR71","doi-asserted-by":"publisher","first-page":"107536","DOI":"10.1016\/j.knosys.2021.107536","volume":"233","author":"Z Xu","year":"2021","unstructured":"Z. Xu, Z. Wang, J. Li, T. Jin, X. Meng, S. Gao, Dendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification. Knowl. Based Syst. 233, 107536 (2021). https:\/\/doi.org\/10.1016\/j.knosys.2021.107536","journal-title":"Knowl. Based Syst."},{"key":"9517_CR72","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1007\/s10489-014-0613-2","volume":"42","author":"S Chen","year":"2015","unstructured":"S. Chen, J. Montgomery, A. Boluf\u00e9-R\u00f6hler, Measuring the curse of dimensionality and its effects on particle swarm optimization and differential evolution. Appl. Intell. 42, 514\u2013526 (2015). https:\/\/doi.org\/10.1007\/s10489-014-0613-2","journal-title":"Appl. Intell."},{"key":"9517_CR73","doi-asserted-by":"publisher","first-page":"105499","DOI":"10.1016\/j.asoc.2019.105499","volume":"81","author":"D Tang","year":"2019","unstructured":"D. Tang, Spherical evolution for solving continuous optimization problems. Appl. Soft Comput. 81, 105499 (2019). https:\/\/doi.org\/10.1016\/j.asoc.2019.105499","journal-title":"Appl. Soft Comput."},{"issue":"21","key":"9517_CR74","doi-asserted-by":"publisher","first-page":"32841","DOI":"10.1007\/s11042-021-11218-y","volume":"80","author":"J Tian","year":"2021","unstructured":"J. Tian, Y. Lu, X. Zuo, Y. Liu, B. Qiao, M. Fan, Q. Ge, S. Fan, A novel image encryption algorithm using pwlcm map-based cml chaotic system and dynamic dna encryption. Multimed. Tools Appl. 80(21), 32841\u201332861 (2021). https:\/\/doi.org\/10.1007\/s11042-021-11218-y","journal-title":"Multimed. Tools Appl."},{"key":"9517_CR75","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.ijleo.2019.03.065","volume":"184","author":"A Hasheminejad","year":"2019","unstructured":"A. Hasheminejad, M. Rostami, A novel bit level multiphase algorithm for image encryption based on pwlcm chaotic map. Optik 184, 205\u2013213 (2019). https:\/\/doi.org\/10.1016\/j.ijleo.2019.03.065","journal-title":"Optik"},{"issue":"06","key":"9517_CR76","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1142\/S0218127495001198","volume":"5","author":"A Baranovsky","year":"1995","unstructured":"A. Baranovsky, D. Daems, Design of one-dimensional chaotic maps with prescribed statistical properties. Int. J. Bifurc. Chaos 5(06), 1585\u20131598 (1995). https:\/\/doi.org\/10.1142\/S0218127495001198","journal-title":"Int. J. Bifurc. Chaos"},{"key":"9517_CR77","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1016\/j.enconman.2019.06.082","volume":"196","author":"X Ju","year":"2019","unstructured":"X. Ju, F. Liu, L. Wang, W.-J. Lee, Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners. Energy Convers. Manag. 196, 1267\u20131281 (2019). https:\/\/doi.org\/10.1016\/j.enconman.2019.06.082","journal-title":"Energy Convers. Manag."},{"key":"9517_CR78","unstructured":"UCI Machine Learning Repository (2023). https:\/\/archive.ics.uci.edu\/"},{"issue":"3","key":"9517_CR79","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/0925-2312(94)00013-I","volume":"7","author":"JF Khaw","year":"1995","unstructured":"J.F. Khaw, B. Lim, L.E. Lim, Optimal design of neural networks using the Taguchi method. Neurocomputing 7(3), 225\u2013245 (1995). https:\/\/doi.org\/10.1016\/0925-2312(94)00013-I","journal-title":"Neurocomputing"},{"key":"9517_CR80","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10732-008-9080-4","volume":"15","author":"S Garc\u00eda","year":"2009","unstructured":"S. Garc\u00eda, D. Molina, M. Lozano, F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms\u2019 behaviour: a case study on the cec\u20192005 special session on real parameter optimization. J. Heuristics 15, 617\u2013644 (2009). https:\/\/doi.org\/10.1007\/s10732-008-9080-4","journal-title":"J. Heuristics"},{"key":"9517_CR81","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"S. Holm, A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65\u201370 (1979)","journal-title":"Scand. J. Stat."},{"key":"9517_CR82","unstructured":"D. Groppe, Bonferroni-Holm Correction for Multiple Comparisons. MATLAB Central File Exchange (2025). https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/28303-bonferroni-holm-correction-for-multiple-comparisons"},{"key":"9517_CR83","doi-asserted-by":"publisher","unstructured":"G.E. Noether, Introduction to Wilcoxon (1945) Individual Comparisons by Ranking Methods (Springer, New York, 1992), pp. 191\u2013195. https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_15","DOI":"10.1007\/978-1-4612-4380-9_15"}],"container-title":["Genetic Programming and Evolvable Machines"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-025-09517-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10710-025-09517-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10710-025-09517-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:34:01Z","timestamp":1767846841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10710-025-09517-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,4]]},"references-count":83,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["9517"],"URL":"https:\/\/doi.org\/10.1007\/s10710-025-09517-6","relation":{},"ISSN":["1389-2576","1573-7632"],"issn-type":[{"value":"1389-2576","type":"print"},{"value":"1573-7632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,4]]},"assertion":[{"value":"28 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"19"}}