{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:26:07Z","timestamp":1776443167599,"version":"3.51.2"},"reference-count":169,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A20306"],"award-info":[{"award-number":["U20A20306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Membr Comput"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s41965-024-00172-x","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T21:07:15Z","timestamp":1731445635000},"page":"135-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial evolutionary intelligence (AEI): evolutionary computation evolves with large language models"],"prefix":"10.1007","volume":"7","author":[{"given":"Cheng","family":"He","sequence":"first","affiliation":[]},{"given":"Ye","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Zhichao","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"172_CR1","doi-asserted-by":"crossref","unstructured":"Das, S., Abraham, A., & Panigrahi, B. (2010). Computational intelligence: Foundations, perspectives, and recent trends. Computational Intelligence and Pattern Analysis in Biological Informatics (pp. 1\u201337).","DOI":"10.1002\/9780470872352.ch1"},{"key":"172_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44874-8","volume-title":"Introduction to Evolutionary Computing","author":"AE Eiben","year":"2015","unstructured":"Eiben, A. E., & Smith, J. E. (2015). Introduction to Evolutionary Computing. Berlin: Springer."},{"key":"172_CR3","doi-asserted-by":"crossref","unstructured":"Porto, V. W. (2018). Evolutionary programming. In Evolutionary computation (Vol. 1, pp. 127\u2013 140). CRC Press.","DOI":"10.1201\/9781482268713-17"},{"issue":"3","key":"172_CR4","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TEVC.2022.3225509","volume":"27","author":"Y Mei","year":"2022","unstructured":"Mei, Y., Chen, Q., Lensen, A., Xue, B., & Zhang, M. (2022). Explainable artificial intelligence by genetic programming: A survey. IEEE Transactions on Evolutionary Computation, 27(3), 621\u2013641.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1023\/A:1015059928466","volume":"1","author":"HG Beyer","year":"2002","unstructured":"Beyer, H. G., & Schwefel, H. P. (2002). Evolution strategies-a comprehensive introduction. Natural Computing, 1, 3\u201352.","journal-title":"Natural Computing"},{"key":"172_CR6","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s11047-013-9398-1","volume":"12","author":"R Doursat","year":"2013","unstructured":"Doursat, R., Sayama, H., & Michel, O. (2013). A review of morphogenetic engineering. Natural Computing, 12, 517\u2013535.","journal-title":"Natural Computing"},{"issue":"7674","key":"172_CR7","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1038\/nature23911","volume":"550","author":"N Aage","year":"2017","unstructured":"Aage, N., Andreassen, E., Lazarov, B. S., & Sigmund, O. (2017). Giga-voxel computational morphogenesis for structural design. Nature, 550(7674), 84\u201386.","journal-title":"Nature"},{"issue":"2","key":"172_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3603704","volume":"56","author":"N Li","year":"2023","unstructured":"Li, N., Ma, L., Yu, G., Xue, B., Zhang, M., & Jin, Y. (2023). Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues. ACM Computing Surveys, 56(2), 1\u201334.","journal-title":"ACM Computing Surveys"},{"issue":"531","key":"172_CR9","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1086\/279202","volume":"45","author":"W Johannsen","year":"1911","unstructured":"Johannsen, W. (1911). The genotype conception of heredity. The American Naturalist, 45(531), 129\u2013159.","journal-title":"The American Naturalist"},{"issue":"160","key":"172_CR10","doi-asserted-by":"publisher","first-page":"20190332","DOI":"10.1098\/rsif.2019.0332","volume":"16","author":"D Nichol","year":"2019","unstructured":"Nichol, D., Robertson-Tessi, M., Anderson, A. R., & Jeavons, P. (2019). Model genotype-phenotype mappings and the algorithmic structure of evolution. Journal of the Royal Society Interface, 16(160), 20190332.","journal-title":"Journal of the Royal Society Interface"},{"issue":"3","key":"172_CR11","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2005.857695","volume":"10","author":"PP Bonissone","year":"2006","unstructured":"Bonissone, P. P., Subbu, R., Eklund, N., & Kiehl, T. R. (2006). Evolutionary algorithms $$+$$ domain knowledge $$=$$ real-world evolutionary computation. IEEE Transactions on Evolutionary Computation, 10(3), 256\u2013280.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR12","doi-asserted-by":"crossref","unstructured":"He, C., Li, H., Lin, J., & Lu, Z. (2023). Long short-term memory network assisted evolutionary algorithm for computationally expensive multiobjective optimization. In 2023 IEEE symposium series on computational intelligence (pp. 972\u2013 978). IEEE","DOI":"10.1109\/SSCI52147.2023.10371889"},{"issue":"2","key":"172_CR13","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TEVC.2013.2248012","volume":"18","author":"B Liu","year":"2014","unstructured":"Liu, B., Zhang, Q., & Gielen, G. G. E. (2014). A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems. IEEE Transactions on Evolutionary Computation, 18(2), 180\u2013192.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR14","doi-asserted-by":"crossref","unstructured":"Boulesnane, A. (2024). Evolutionary dynamic optimization and machine learning. In Advanced machine learning with evolutionary and metaheuristic techniques (pp. 67\u2013 85). Springer","DOI":"10.1007\/978-981-99-9718-3_3"},{"issue":"3","key":"172_CR15","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s11831-023-10017-y","volume":"31","author":"A Mahdavi-Meymand","year":"2024","unstructured":"Mahdavi-Meymand, A., Sulisz, W., & Zounemat-Kermani, M. (2024). Hybrid and integrative evolutionary machine learning in hydrology: A systematic review and meta-analysis. Archives of Computational Methods in Engineering, 31(3), 1297\u20131340.","journal-title":"Archives of Computational Methods in Engineering"},{"key":"172_CR16","doi-asserted-by":"crossref","unstructured":"Mirjalili, S. Z., Sajeev, S., Saha, R., Khodadadi, N., Mirjalili, S. M., & Mirjalili, S. (2022). Evolutionary population dynamic mechanisms for the harmony search algorithm. In Proceedings of 7th international conference on harmony search, soft computing and applications (pp. 185\u2013194). Springer","DOI":"10.1007\/978-981-19-2948-9_18"},{"key":"172_CR17","doi-asserted-by":"crossref","unstructured":"Kneissl, C., Sudholt, D. (2023). The cost of randomness in evolutionary algorithms: Crossover can save random bits. In European conference on evolutionary computation in combinatorial optimization (part Of Evostar) (pp. 179\u2013 194). Springer.","DOI":"10.1007\/978-3-031-30035-6_12"},{"key":"172_CR18","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.swevo.2015.06.002","volume":"25","author":"I Zelinka","year":"2015","unstructured":"Zelinka, I. (2015). A survey on evolutionary algorithms dynamics and its complexity-mutual relations, past, present and future. Swarm and Evolutionary Computation, 25, 2\u201314.","journal-title":"Swarm and Evolutionary Computation"},{"key":"172_CR19","unstructured":"Wang, Y., Chen, W., Han, X., Lin, X., Zhao, H., Liu, Y., Zhai, B., Yuan, J., You, Q., & Yang, H. (2024). Exploring the reasoning abilities of multimodal large language models (MLLMs): A comprehensive survey on emerging trends in multimodal reasoning. arXiv:2401.06805"},{"key":"172_CR20","unstructured":"Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023). The rise and potential of large language model based agents: A survey. arXiv:2309.07864"},{"key":"172_CR21","unstructured":"Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023). A survey of large language models. arXiv:2303.18223"},{"key":"172_CR22","unstructured":"Muktadir, G. M. (2023). A brief history of prompt: Leveraging language models. arXiv:2310.04438"},{"key":"172_CR23","doi-asserted-by":"crossref","unstructured":"Bhattacharya, P., Prasad, V. K., Verma, A., Gupta, D., Sapsomboon, A., Viriyasitavat, W., & Dhiman, G. (2024). Demystifying ChatGPT: An In-depth Survey of OpenAI\u2019s Robust Large Language Models. Archives of Computational Methods in Engineering, (pp. 1\u201344)","DOI":"10.1007\/s11831-024-10115-5"},{"issue":"7553","key":"172_CR24","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1038\/nature14544","volume":"521","author":"AE Eiben","year":"2015","unstructured":"Eiben, A. E., & Smith, J. (2015). From evolutionary computation to the evolution of things. Nature, 521(7553), 476\u2013482.","journal-title":"Nature"},{"issue":"6","key":"172_CR25","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1109\/TCYB.2020.2985081","volume":"51","author":"C He","year":"2020","unstructured":"He, C., Huang, S., Cheng, R., Tan, K. C., & Jin, Y. (2020). Evolutionary multiobjective optimization driven by generative adversarial networks (GANs). IEEE Transactions on Cybernetics, 51(6), 3129\u20133142.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"172_CR26","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2018","unstructured":"Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., & Jin, Y. (2018). A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 23(1), 74\u201388.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR27","doi-asserted-by":"crossref","unstructured":"Dang, D.-C., Eremeev, A., & Lehre, P. K. (2021). Escaping local optima with non-elitist evolutionary algorithms. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, pp. 12275\u2013 12283).","DOI":"10.1609\/aaai.v35i14.17457"},{"key":"172_CR28","doi-asserted-by":"crossref","unstructured":"Roy, P., Hussein, R., & Deb, K. (2017). Metamodeling for multimodal selection functions in evolutionary multi-objective optimization. In Proceedings of the genetic and evolutionary computation conference (pp. 625\u2013 632).","DOI":"10.1145\/3071178.3071219"},{"key":"172_CR29","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.engappai.2016.08.012","volume":"56","author":"GJ Krishna","year":"2016","unstructured":"Krishna, G. J., & Ravi, V. (2016). Evolutionary computing applied to customer relationship management: A survey. Engineering Applications of Artificial Intelligence, 56, 30\u201359.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"10","key":"172_CR30","doi-asserted-by":"publisher","first-page":"7672","DOI":"10.1109\/TIE.2018.2801805","volume":"65","author":"G Bramerdorfer","year":"2018","unstructured":"Bramerdorfer, G., Tapia, J. A., Pyrh\u00f6nen, J. J., & Cavagnino, A. (2018). Modern electrical machine design optimization: Techniques, trends, and best practices. IEEE Transactions on Industrial Electronics, 65(10), 7672\u20137684.","journal-title":"IEEE Transactions on Industrial Electronics"},{"issue":"5","key":"172_CR31","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/TSMCC.2005.855515","volume":"36","author":"SK Pal","year":"2006","unstructured":"Pal, S. K., Bandyopadhyay, S., & Ray, S. S. (2006). Evolutionary computation in bioinformatics: A review. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(5), 601\u2013615.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"172_CR32","doi-asserted-by":"crossref","unstructured":"Zhan, Z.-H., Shi, L., Tan, K. C., & Zhang, J. (2022). A survey on evolutionary computation for complex continuous optimization. Artificial Intelligence Review, 55(1), 59-110.","DOI":"10.1007\/s10462-021-10042-y"},{"key":"172_CR33","doi-asserted-by":"crossref","unstructured":"Goldberg, D. E. (1990). The theory of virtual alphabets. In International conference on parallel problem solving from nature (pp. 13\u2013 22). Springer.","DOI":"10.1007\/BFb0029726"},{"issue":"2","key":"172_CR34","first-page":"33","volume":"3","author":"U Aiman","year":"2015","unstructured":"Aiman, U., & Asrar, N. (2015). Genetic algorithm based solution to SAT-3 problem. Journal of Computer Sciences and Applications, 3(2), 33\u201339.","journal-title":"Journal of Computer Sciences and Applications"},{"issue":"3","key":"172_CR35","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/0377-2217(95)00077-1","volume":"93","author":"S Chatterjee","year":"1996","unstructured":"Chatterjee, S., Carrera, C., & Lynch, L. A. (1996). Genetic algorithms and traveling salesman problems. European Journal of Operational Research, 93(3), 490\u2013510.","journal-title":"European Journal of Operational Research"},{"key":"172_CR36","unstructured":"Bledsoe, W. W. (1961). The use of biological concepts in the analytical study of systems. In The ORSA-TIMS National Meeting."},{"issue":"2","key":"172_CR37","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1162\/106365601750190406","volume":"9","author":"K Deb","year":"2001","unstructured":"Deb, K., & Beyer, H.-G. (2001). Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation, 9(2), 197\u2013221.","journal-title":"Evolutionary Computation"},{"issue":"1","key":"172_CR38","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s42256-020-00278-8","volume":"3","author":"R Miikkulainen","year":"2021","unstructured":"Miikkulainen, R., & Forrest, S. (2021). A biological perspective on evolutionary computation. Nature Machine Intelligence, 3(1), 9\u201315.","journal-title":"Nature Machine Intelligence"},{"issue":"2","key":"172_CR39","doi-asserted-by":"publisher","first-page":"1814","DOI":"10.1109\/TETCI.2024.3358377","volume":"8","author":"H Li","year":"2024","unstructured":"Li, H., Lin, J., Chen, Q., He, C., & Pan, L. (2024). Supervised reconstruction for high-dimensional expensive multiobjective optimization. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(2), 1814\u20131827.","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"issue":"3","key":"172_CR40","doi-asserted-by":"publisher","first-page":"0174635","DOI":"10.1371\/journal.pone.0174635","volume":"12","author":"L Helms","year":"2017","unstructured":"Helms, L., & Clune, J. (2017). Improving HybrID: How to best combine indirect and direct encoding in evolutionary algorithms. PLoS ONE, 12(3), 0174635.","journal-title":"PLoS ONE"},{"issue":"4","key":"172_CR41","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s40747-018-0080-1","volume":"4","author":"R Cheng","year":"2018","unstructured":"Cheng, R., He, C., Jin, Y., & Yao, X. (2018). Model-based evolutionary algorithms: A short survey. Complex & Intelligent Systems, 4(4), 283\u2013292.","journal-title":"Complex & Intelligent Systems"},{"key":"172_CR42","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H., Yu, Y., & Qian, C. (2019). Evolutionary learning: Advances in theories and algorithms (pp. 3-10). Singapore: Springer Singapore.","DOI":"10.1007\/978-981-13-5956-9_1"},{"issue":"8","key":"172_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3467477","volume":"54","author":"A Telikani","year":"2021","unstructured":"Telikani, A., Tahmassebi, A., Banzhaf, W., & Gandomi, A. H. (2021). Evolutionary machine learning: A survey. ACM Computing Surveys (CSUR), 54(8), 1\u201335.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"172_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101606","volume":"88","author":"S Shao","year":"2024","unstructured":"Shao, S., Tian, Y., & Zhang, X. (2024). Deep reinforcement learning assisted automated guiding vector selection for large-scale sparse multi-objective optimization. Swarm and Evolutionary Computation, 88, 101606.","journal-title":"Swarm and Evolutionary Computation"},{"issue":"suppl-1","key":"172_CR45","doi-asserted-by":"publisher","first-page":"8597","DOI":"10.1073\/pnas.0702207104","volume":"104","author":"M Lynch","year":"2007","unstructured":"Lynch, M. (2007). The frailty of adaptive hypotheses for the origins of organismal complexity. Proceedings of the National Academy of Sciences, 104(suppl-1), 8597\u20138604.","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"172_CR46","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/S1045-926X(02)00060-5","volume":"14","author":"TD Collins","year":"2003","unstructured":"Collins, T. D. (2003). Applying software visualization technology to support the use of evolutionary algorithms. Journal of Visual Languages & Computing, 14(2), 123\u2013150.","journal-title":"Journal of Visual Languages & Computing"},{"key":"172_CR47","unstructured":"Cartwright, H. M. (1991). Looking around: Using clues from the data space to guide genetic algorithm searches. In Proceedings of the 4th international conference on genetic algorithms, (pp. 108\u2013114)."},{"key":"172_CR48","doi-asserted-by":"crossref","unstructured":"Vassilev, V. K., Fogarty, T. C., & Miller, J. F. (2003). Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application.A dvances in evolutionary computing: theory and applications, (pp. 3\u201344).","DOI":"10.1007\/978-3-642-18965-4_1"},{"key":"172_CR49","doi-asserted-by":"crossref","unstructured":"Yao, X. (2012). Unpacking and understanding evolutionary algorithms. In IEEE world congress on computational intelligence (pp. 60\u2013 76). Springer.","DOI":"10.1007\/978-3-642-30687-7_4"},{"key":"172_CR50","doi-asserted-by":"crossref","unstructured":"Nayyar, A., Garg, S., Gupta, D., & Khanna, A. (2018). Evolutionary computation: Theory and algorithms. In Advances in warm intelligence for optimizing problems in computer science (pp. 1\u2013 26). Chapman and Hall\/CRC.","DOI":"10.1201\/9780429445927-1"},{"key":"172_CR51","unstructured":"White, D. (2014). An overview of schema theory. arXiv:1401.2651"},{"key":"172_CR52","doi-asserted-by":"crossref","unstructured":"Ochoa, G., & Malan, K. (2019). Recent advances in fitness landscape analysis. In Proceedings of the genetic and evolutionary computation conference companion (pp. 1077\u2013 1094).","DOI":"10.1145\/3319619.3323383"},{"issue":"1","key":"172_CR53","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67\u201382.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"4","key":"172_CR54","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1162\/EVCO_a_00132","volume":"22","author":"H-G Beyer","year":"2014","unstructured":"Beyer, H.-G. (2014). Convergence analysis of evolutionary algorithms that are based on the paradigm of information geometry. Evolutionary Computation, 22(4), 679\u2013709.","journal-title":"Evolutionary Computation"},{"issue":"1","key":"172_CR55","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/S0004-3702(01)00058-3","volume":"127","author":"J He","year":"2001","unstructured":"He, J., & Yao, X. (2001). Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence, 127(1), 57\u201385.","journal-title":"Artificial Intelligence"},{"key":"172_CR56","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.swevo.2019.04.008","volume":"48","author":"J Del Ser","year":"2019","unstructured":"Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello, C. A. C., & Herrera, F. (2019). Bio-inspired computation: Where we stand and what\u2019s next. Swarm and Evolutionary Computation, 48, 220\u2013250.","journal-title":"Swarm and Evolutionary Computation"},{"key":"172_CR57","unstructured":"Chang, Y., Wang, X., Wang, J., Wu, Y., Zhu, K., Chen, H., Yang, L., Yi, X., Wang, C., Wang, Y., et al. (2023). A survey on evaluation of large language models. arXiv:2307.03109"},{"key":"172_CR58","doi-asserted-by":"crossref","unstructured":"Borzunov, A., Ryabinin, M., Chumachenko, A., Baranchuk, D., Dettmers, T., Belkada, Y., Samygin, P., & Raffel, C. A. (2024). Distributed inference and fine-tuning of large language models over the internet. In Advances in neural information processing systems (Vol 36).","DOI":"10.18653\/v1\/2023.acl-demo.54"},{"key":"172_CR59","unstructured":"Goertzel, B. (2023). Generative AI vs. AGI: The cognitive strengths and weaknesses of modern LLMs. arXiv:2309.10371"},{"key":"172_CR60","doi-asserted-by":"crossref","unstructured":"Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. arXiv:1909.00512","DOI":"10.18653\/v1\/D19-1006"},{"key":"172_CR61","unstructured":"Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., Amodei, D. (2020). Scaling laws for neural language models. arXiv:2001.08361"},{"key":"172_CR62","unstructured":"Wang, X., Li, C., Wang, Z., Bai, F., Luo, H., Zhang, J., Jojic, N., Xing, E. P., & Hu, Z. (2023). PromptAgent: Strategic planning with language models enables expert-level prompt optimization. arXiv:2310.16427"},{"key":"172_CR63","unstructured":"Wang, X., Li, C., Wang, Z., Bai, F., Luo, H., Zhang, J., Jojic, N., Xing, E. P., & Hu, Z. (2023). Promptagent: Strategic planning with language models enables expert-level prompt optimization. arXiv:2310.16427"},{"key":"172_CR64","unstructured":"Guo, P.-F., Chen, Y.-H., Tsai, Y.-D., & Lin, S.-D. (2023). Towards optimizing with large language models. arXiv:2310.05204"},{"key":"172_CR65","unstructured":"Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., & McHardy, R. (2023). Challenges and applications of large language models. arXiv:2307.10169"},{"key":"172_CR66","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv:2307.09288"},{"key":"172_CR67","doi-asserted-by":"crossref","unstructured":"Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., & Stanley, K. O. (2023). Evolution through large models. In Handbook of evolutionary machine learning (pp. 331\u2013 366). Springer.","DOI":"10.1007\/978-981-99-3814-8_11"},{"key":"172_CR68","unstructured":"Luo, Z., Xu, C., Zhao, P., Sun, Q., Geng, X., Hu, W., Tao, C., Ma, J., Lin, Q., & Jiang, D. (2023). Wizardcoder: Empowering code large language models with evol-instruct. arXiv:2306.08568"},{"key":"172_CR69","doi-asserted-by":"crossref","unstructured":"Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J.S., Wang, P., Fawzi, O., Kohli, P., & Fawzi, A. (2023). Mathematical discoveries from program search with large language models. Nature, 625(7995), 468\u2013475","DOI":"10.1038\/s41586-023-06924-6"},{"key":"172_CR70","doi-asserted-by":"crossref","unstructured":"Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J.S., Wang, P., Fawzi, O., Kohli, P., & Fawzi, A. (2023). Mathematical discoveries from program search with large language models. Nature.","DOI":"10.1038\/s41586-023-06924-6"},{"key":"172_CR71","unstructured":"Chen, A., Dohan, D., & So, D. (2023). EvoPrompting: Language models for code-level neural architecture search. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in neural information processing systems (Vol. 36, pp. 7787\u20137817). Curran Associates Inc"},{"key":"172_CR72","first-page":"7787","volume-title":"Advances in neural information processing systems","author":"A Chen","year":"2023","unstructured":"Chen, A., Dohan, D., & So, D. (2023). Evoprompting: Language models for code-level neural architecture search. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Advances in neural information processing systems (Vol. 36, pp. 7787\u20137817). Curran Associates Inc."},{"key":"172_CR73","doi-asserted-by":"crossref","unstructured":"Yang, H., & Li, K. (2023). InstOptima: Evolutionary multi-objective instruction optimization via large language model-based instruction operators. arXiv:2310.17630","DOI":"10.18653\/v1\/2023.findings-emnlp.907"},{"key":"172_CR74","unstructured":"Fernando, C., Banarse, D., Michalewski, H., Osindero, S., & Rockt\u00e4schel, T. (2023). Promptbreeder: Self-referential self-improvement via prompt evolution. arXiv:2309.16797"},{"key":"172_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, S., Yu, W., Xu, Y., Iter, D., Zeng, Q., Liu, Y., Zhu, C., & Jiang, M. (2023). Auto-instruct: Automatic instruction generation and ranking for black-box language models. arXiv:2310.13127","DOI":"10.18653\/v1\/2023.findings-emnlp.659"},{"key":"172_CR76","unstructured":"Ma, Z., Guo, H., Chen, J., Peng, G., Cao, Z., Ma, Y., & Gong, Y.-J. (2024) LLaMoCo: Instruction tuning of large language models for optimization code generation. arXiv:2403.01131"},{"key":"172_CR77","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, F., Liu, Z., Wang, K., Wang, F., & Qu, X. (2023). Can language models be used for real-world urban-delivery route optimization? The Innovation, 4(6).","DOI":"10.1016\/j.xinn.2023.100520"},{"key":"172_CR78","unstructured":"Zhao, Z., Lee, W. S., & Hsu, D. (2023). Large language models as commonsense knowledge for large-scale task planning. In Advances in neural information processing systems (vol. 36). Curran Associates, Inc."},{"key":"172_CR79","unstructured":"Zhang, M. R., Desai, N., Bae, J., Lorraine, J., & Ba, J. (2023). Using large language models for hyperparameter optimization. In NeurIPS 2023 foundation models for decision making workshop."},{"key":"172_CR80","doi-asserted-by":"crossref","unstructured":"Yao, Y., Liu, F., Cheng, J., & Zhang, Q. (2024). Evolve cost-aware acquisition functions using large language models. arXiv:2404.16906","DOI":"10.1007\/978-3-031-70068-2_23"},{"key":"172_CR81","unstructured":"Chao, W., Zhao, J., Jiao, L., Li, L., Liu, F., & Yang, S. (2024). A match made in consistency heaven: when large language models meet evolutionary algorithms. arXiv:2401.10510"},{"key":"172_CR82","doi-asserted-by":"crossref","unstructured":"Brownlee, A. E., Callan, J., Even-Mendoza, K., Geiger, A., Hanna, C., Petke, J., Sarro, F., Sobania, D. (2023). Enhancing genetic improvement mutations using large language models. In International symposium on search based software engineering (pp. 153\u2013 159). Springer.","DOI":"10.1007\/978-3-031-48796-5_13"},{"key":"172_CR83","unstructured":"Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., & Chen, X. (2023). Large language models as optimizers. arXiv:2309.03409"},{"key":"172_CR84","doi-asserted-by":"crossref","unstructured":"Wang, Z., Liu, S., Chen, J., & Tan, K. C. (2024). Large language model-aided evolutionary search for constrained multiobjective optimization. arXiv:2405.05767","DOI":"10.1007\/978-981-97-5581-3_18"},{"key":"172_CR85","unstructured":"Ye, H., Wang, J., Cao, Z., & Song, G. (2024). ReEvo: Large language models as hyper-heuristics with reflective evolution. arXiv:2402.01145"},{"key":"172_CR86","unstructured":"Liu, F., Tong, X., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z., & Zhang, Q. (2024). Evolution of heuristics: Towards efficient automatic algorithm design using large language model. In Forty-first International Conference on Machine Learning."},{"key":"172_CR87","unstructured":"Liu, F., Tong, X., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z., & Zhang, Q. (2024). Evolution of heuristics: Towards efficient automatic algorithm design using large language model. PMLR."},{"key":"172_CR88","unstructured":"Liu, F., Tong, X., Yuan, M., & Zhang, Q. (2023). Algorithm evolution using large language model. arXiv:2311.15249"},{"key":"172_CR89","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhong, Y., Wu, J., & Tan, K. C. (2023). Large language model-enhanced algorithm selection: Towards comprehensive algorithm representation. arXiv:2311.13184","DOI":"10.24963\/ijcai.2024\/579"},{"key":"172_CR90","unstructured":"Wu, X., Zhong, Y., Wu, J., Huang, Y., Wu, S., & Tan, K. C. (2024). Unlock the power of algorithm features: A generalization analysis for algorithm selection. arXiv:2405.11349"},{"issue":"2","key":"172_CR91","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2019","unstructured":"Baltru\u0161aitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423\u2013443.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"172_CR92","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/BF00175355","volume":"4","author":"JR Koza","year":"1994","unstructured":"Koza, J. R. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87\u2013112.","journal-title":"Statistics and Computing"},{"key":"172_CR93","doi-asserted-by":"crossref","unstructured":"Kelly, S., & Heywood, M. I. (2017). Emergent tangled graph representations for atari game playing agents. In Proceedings of 20th European conference on genetic programming (pp. 64 - 79). Springer.","DOI":"10.1007\/978-3-319-55696-3_5"},{"key":"172_CR94","unstructured":"Liu, F., Xialiang, T., Yuan, M., Lin, X., Luo, F., Wang, Z., Lu, Z., & Zhang, Q. (2024). Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model. In Fortyfirst international conference on machine learning."},{"key":"172_CR95","doi-asserted-by":"crossref","unstructured":"McKinzie, B., Gan, Z., Fauconnier, J.-P., Dodge, S., Zhang, B., Dufter, P., Shah, D., Du, X., Peng, F., Weers, F., et al. (2024). Mm1: Methods, analysis & insights from multimodal llm pre-training. arXiv:2403.09611","DOI":"10.1007\/978-3-031-73397-0_18"},{"key":"172_CR96","doi-asserted-by":"crossref","unstructured":"Belyaeva, A., Cosentino, J., Hormozdiari, F., Eswaran, K., Shetty, S., Corrado, G., Carroll, A., McLean, C. Y., & Furlotte, N. A. (2023). Multimodal LLMs for health grounded in individual-specific data. In Workshop on Machine Learning for Multimodal Healthcare Data (pp. 86\u2013 102). Springer.","DOI":"10.1007\/978-3-031-47679-2_7"},{"key":"172_CR97","doi-asserted-by":"crossref","unstructured":"Wu, X., Wu, S.-h., Wu, J., Feng, L., & Tan, K. C. (2024). Evolutionary computation in the era of large language model: Survey and roadmap. arXiv:2401.10034","DOI":"10.1109\/TEVC.2024.3506731"},{"key":"172_CR98","doi-asserted-by":"crossref","unstructured":"Tabti, H., EL\u00a0Bourakkadi, H., Chemlal, A., Jarjar, A., Zenkouar, K., & Najah, S. (2024). Genetic crossover at the rna level for secure medical image encryption. International Journal of Safety & Security Engineering, 14(1).","DOI":"10.18280\/ijsse.140120"},{"issue":"8","key":"172_CR99","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1145\/3470971","volume":"54","author":"Y Tian","year":"2022","unstructured":"Tian, Y., Si, L., Zhang, X., Cheng, R., He, C., Tan, K. C., & Jin, Y. (2022). Evolutionary large-scale multi-objective optimization: A survey. ACM Computing Surveys, 54(8), 174.","journal-title":"ACM Computing Surveys"},{"key":"172_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119495","volume":"217","author":"C He","year":"2023","unstructured":"He, C., Zhang, Y., Gong, D., & Ji, X. (2023). A review of surrogate-assisted evolutionary algorithms for expensive optimization problems. Expert Systems with Applications, 217, 119495.","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"172_CR101","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1109\/TSMC.2020.3044418","volume":"52","author":"D Guo","year":"2021","unstructured":"Guo, D., Wang, X., Gao, K., Jin, Y., Ding, J., & Chai, T. (2021). Evolutionary optimization of high-dimensional multiobjective and many-objective expensive problems assisted by a dropout neural network. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(4), 2084\u20132097.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"172_CR102","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100774","volume":"60","author":"F Li","year":"2021","unstructured":"Li, F., Gao, L., Garg, A., Shen, W., & Huang, S. (2021). Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions. Swarm and Evolutionary Computation, 60, 100774.","journal-title":"Swarm and Evolutionary Computation"},{"key":"172_CR103","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101323","volume":"80","author":"Y Tian","year":"2023","unstructured":"Tian, Y., Hu, J., He, C., Ma, H., Zhang, L., & Zhang, X. (2023). A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization. Swarm and Evolutionary Computation, 80, 101323.","journal-title":"Swarm and Evolutionary Computation"},{"issue":"4","key":"172_CR104","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TEVC.2005.859463","volume":"10","author":"MTM Emmerich","year":"2006","unstructured":"Emmerich, M. T. M., Giannakoglou, K. C., & Naujoks, B. (2006). Single- and multi-objective evolutionary optimization assisted by Gaussian random field metamodels. IEEE Transactions on Evolutionary Computation, 10(4), 421\u2013439.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"33","key":"172_CR105","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1016\/j.cma.2007.12.014","volume":"197","author":"GP Liu","year":"2008","unstructured":"Liu, G. P., Han, X., & Jiang, C. (2008). A novel multi-objective optimization method based on an approximation model management technique. Computer Methods in Applied Mechanics & Engineering, 197(33), 2719\u20132731.","journal-title":"Computer Methods in Applied Mechanics & Engineering"},{"key":"172_CR106","doi-asserted-by":"crossref","unstructured":"Azzouz, N., Bechikh, S., & Said, L. B. (2014) Steady state IBEA assisted by MLP neural networks for expensive multi-objective optimization problems. In Proceedings of the 2014 conference on genetic and evolutionary computation (pp. 581\u2013 588).","DOI":"10.1145\/2576768.2598271"},{"issue":"1","key":"172_CR107","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jhydrol.2012.10.050","volume":"479","author":"G Kourakos","year":"2013","unstructured":"Kourakos, G., & Mantoglou, A. (2013). Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. Journal of Hydrology, 479(1), 13\u201323.","journal-title":"Journal of Hydrology"},{"key":"172_CR108","doi-asserted-by":"crossref","unstructured":"Loshchilov, I., & Schoenauer, M. (2010). Comparison-based optimizers need comparison-based surrogates. In Proceedings of the 2010 international conference on parallel problem solving from nature (pp. 364\u2013 373).","DOI":"10.1007\/978-3-642-15844-5_37"},{"key":"172_CR109","doi-asserted-by":"crossref","unstructured":"Pavelski, L. M., Delgado, M. R., Almeida, C. P. D., Goncalves, R. A., & Venske, S. M. (2014). ELMOEA\/D-DE: Extreme learning surrogate models in multi-objective optimization based on decomposition and differential evolution. In Proceedings of the 2014 Brazilian conference on intelligent systems (pp. 318\u2013 323).","DOI":"10.1109\/BRACIS.2014.64"},{"key":"172_CR110","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.eswa.2016.03.044","volume":"57","author":"R Datta","year":"2016","unstructured":"Datta, R., & Regis, R. G. (2016). A surrogate-assisted evolution strategy for constrained multi-objective optimization. Expert Systems with Applications, 57, 270\u2013284.","journal-title":"Expert Systems with Applications"},{"key":"172_CR111","unstructured":"Emami, P., Li, Z., Sinha, S., & Nguyen, T. (2024). SysCaps: Language interfaces for simulation surrogates of complex systems. arXiv:2405.19653"},{"issue":"6","key":"172_CR112","doi-asserted-by":"publisher","first-page":"3115","DOI":"10.1109\/TCYB.2020.2979930","volume":"51","author":"Y Tian","year":"2021","unstructured":"Tian, Y., Lu, C., Zhang, X., Tan, K. C., & Jin, Y. (2021). Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Transactions on Cybernetics, 51(6), 3115\u20133128.","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"172_CR113","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TEVC.2017.2704782","volume":"22","author":"H Zille","year":"2018","unstructured":"Zille, H., Ishibuchi, H., Mostaghim, S., & Nojima, Y. (2018). A framework for large-scale multiobjective optimization based on problem transformation. IEEE Transactions on Evolutionary Computation, 22(2), 260\u2013275.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"6","key":"172_CR114","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/TEVC.2019.2896002","volume":"23","author":"C He","year":"2019","unstructured":"He, C., Li, L., Tian, Y., Zhang, X., Cheng, R., Jin, Y., & Yao, X. (2019). Accelerating large-scale multiobjective optimization via problem reformulation. IEEE Transactions on Evolutionary Computation, 23(6), 949\u2013961.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR115","doi-asserted-by":"crossref","unstructured":"Qian, H., & Yu, Y. (2017). Solving high-dimensional multi-objective optimization problems with low effective dimensions. In Proceedings of the thirty-first AAAI conference on artificial intelligence (pp. 875\u2013 881). AAAI Press.","DOI":"10.1609\/aaai.v31i1.10664"},{"key":"172_CR116","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106120","volume":"89","author":"R Liu","year":"2020","unstructured":"Liu, R., Ren, R., Liu, J., & Liu, J. (2020). A clustering and dimensionality reduction based evolutionary algorithm for large-scale multi-objective problems. Applied Soft Computing, 89, 106120.","journal-title":"Applied Soft Computing"},{"key":"172_CR117","doi-asserted-by":"publisher","unstructured":"Tian, Y., Wang, L., Yang, S., Ding, J., Jin, Y., & Zhang, X. (2024). Neural network-based dimensionality reduction for large-scale binary optimization with millions of variables. IEEE Transactions on Evolutionary Computation. DOI: https:\/\/doi.org\/10.1109\/TEVC.2024.3400398","DOI":"10.1109\/TEVC.2024.3400398"},{"key":"172_CR118","doi-asserted-by":"crossref","unstructured":"Tian, Y., Wang, L., Yang, S., Ding, J., Jin, Y., & Zhang, X. (2024). Neural network-based dimensionality reduction for large-scale binary optimization with millions of variables. IEEE Transactions on Evolutionary Computation.","DOI":"10.1109\/TEVC.2024.3400398"},{"issue":"2","key":"172_CR119","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TEVC.2019.2921598","volume":"24","author":"C Huang","year":"2020","unstructured":"Huang, C., Li, Y., & Yao, X. (2020). A survey of automatic parameter tuning methods for metaheuristics. IEEE Transactions on Evolutionary Computation, 24(2), 201\u2013216.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR120","doi-asserted-by":"crossref","unstructured":"Sallam, K. M., Elsayed, S. M., Chakrabortty, R. K., & Ryan, M. J. (2020). Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1\u2013 8). IEEE.","DOI":"10.1109\/CEC48606.2020.9185577"},{"key":"172_CR121","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2022.101104","volume":"73","author":"B Wang","year":"2022","unstructured":"Wang, B., Shui, Z., Feng, Y., & Ma, Z. (2022). Evolutionary algorithm with dynamic population size for constrained multiobjective optimization. Swarm and Evolutionary Computation, 73, 101104.","journal-title":"Swarm and Evolutionary Computation"},{"issue":"6","key":"172_CR122","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1109\/TEVC.2016.2521175","volume":"20","author":"R Wang","year":"2016","unstructured":"Wang, R., Zhang, Q., & Zhang, T. (2016). Decomposition-based algorithms using Pareto adaptive scalarizing methods. IEEE Transactions on Evolutionary Computation, 20(6), 821\u2013837.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR123","doi-asserted-by":"crossref","unstructured":"Tian, Y., Yao, L., Shao, S., Zhang, Y., & Zhang, X. (2024) Deep reinforcement learning based adaptive environmental selection for evolutionary multi-objective optimization. In Proceedings of the 2024 IEEE congress on evolutionary computation (pp. 1-8). IEEE.","DOI":"10.1109\/CEC60901.2024.10612045"},{"key":"172_CR124","doi-asserted-by":"crossref","unstructured":"Tian, Y., Yao, L., Shao, S., Zhang, Y., & Zhang, X. (2024) Deep reinforcement learning based adaptive environmental selection for evolutionary multi-objective optimization. In Proceedings of the 2024 IEEE congress on evolutionary computation.","DOI":"10.1109\/CEC60901.2024.10612045"},{"key":"172_CR125","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00483-4","volume-title":"Tuning metaheuristics: A machine learning perspective","author":"M Birattari","year":"2009","unstructured":"Birattari, M. (2009). Tuning metaheuristics: A machine learning perspective. Springer."},{"key":"172_CR126","doi-asserted-by":"crossref","unstructured":"DaCosta, L., Fialho, A., Schoenauer, M., & Sebag, M. (2008). Adaptive operator selection with dynamic multi-armed bandits. In Proceedings of the 10th annual conference on genetic and evolutionary computation (pp. 913\u2013920).","DOI":"10.1145\/1389095.1389272"},{"key":"172_CR127","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.ins.2018.09.005","volume":"471","author":"A Santiago","year":"2019","unstructured":"Santiago, A., Dorronsoro, B., Nebro, A. J., Durillo, J. J., Castillo, O., & Fraire, H. J. (2019). A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME. Information Sciences, 471, 233\u2013251.","journal-title":"Information Sciences"},{"key":"172_CR128","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.neucom.2019.12.048","volume":"384","author":"C Wang","year":"2020","unstructured":"Wang, C., Xu, R., Qiu, J., & Zhang, X. (2020). AdaBoost-inspired multi-operator ensemble strategy for multi-objective evolutionary algorithms. Neurocomputing, 384, 243\u2013255.","journal-title":"Neurocomputing"},{"key":"172_CR129","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, L., He, C., Cheng, R., & Yao, X. (2021). Operator-adapted evolutionary large-scale multiobjective optimization for voltage transformer ratio error estimation. In Proceedings of the 2021 international conference on evolutionary multi-criterion optimization (pp. 672\u2013683)","DOI":"10.1007\/978-3-030-72062-9_53"},{"issue":"4","key":"172_CR130","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/TEVC.2021.3060811","volume":"25","author":"J Sun","year":"2021","unstructured":"Sun, J., Liu, X., B\u00e4ck, T., & Xu, Z. (2021). Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. IEEE Transactions on Evolutionary Computation, 25(4), 666\u2013680.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"172_CR131","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/evco_a_00242","volume":"27","author":"P Kerschke","year":"2018","unstructured":"Kerschke, P., Hoos, H. H., Neumann, F., & Trautmann, H. (2018). Automated algorithm selection: Survey and perspectives. Evolutionary Computation, 27(1), 3\u201345.","journal-title":"Evolutionary Computation"},{"key":"172_CR132","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, H., Xiang, X., Jiang, H., & Zhang, X. (2022). A comparative study on evolutionary algorithms and mathematical programming methods for continuous optimization. In Proceedings of the 2022 IEEE congress on evolutionary computation (pp. 1-8)","DOI":"10.1109\/CEC55065.2022.9870359"},{"key":"172_CR133","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1090.001.0001","volume-title":"Adaptation in natural and artificial systems","author":"JH Holland","year":"1992","unstructured":"Holland, J. H. (1992). Adaptation in natural and artificial systems. MIT Press."},{"issue":"4","key":"172_CR134","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., & Price, K. (1997). Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341\u2013359.","journal-title":"Journal of Global Optimization"},{"key":"172_CR135","doi-asserted-by":"crossref","unstructured":"Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the 6th international symposium on micro machine and human science (pp. 39\u201343).","DOI":"10.1109\/MHS.1995.494215"},{"issue":"1","key":"172_CR136","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/evco_a_00236","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., & Trautmann, H. (2019). Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning. Evolutionary Computation, 27(1), 99\u2013127.","journal-title":"Evolutionary Computation"},{"key":"172_CR137","doi-asserted-by":"crossref","unstructured":"Tian, Y., Peng, S., Rodemann, T., Zhang, X., & Jin, Y. (2019). Automated selection of evolutionary multi-objective optimization algorithms. In Proceedings of the 2019 IEEE symposium series on computational intelligence (pp. 3225\u20133232). IEEE","DOI":"10.1109\/SSCI44817.2019.9003018"},{"key":"172_CR138","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ins.2018.10.013","volume":"476","author":"X Chu","year":"2019","unstructured":"Chu, X., Cai, F., Cui, C., Hu, M., Li, L., & Qin, Q. (2019). Adaptive recommendation model using meta-learning for population-based algorithms. Information Sciences, 476, 192\u2013210.","journal-title":"Information Sciences"},{"issue":"1","key":"172_CR139","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/TAI.2020.3022339","volume":"1","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Peng, S., Zhang, X., Rodemann, T., Tan, K. C., & Jin, Y. (2020). A recommender system for metaheuristic algorithms for continuous optimization based on deep recurrent neural networks. IEEE Transactions on Artificial Intelligence, 1(1), 5\u201318.","journal-title":"IEEE Transactions on Artificial Intelligence"},{"issue":"4","key":"172_CR140","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1109\/TEVC.2022.3186667","volume":"27","author":"K Qiao","year":"2023","unstructured":"Qiao, K., Yu, K., Qu, B., Liang, J., Yue, C., & Ban, X. (2023). Feature extraction for recommendation of constrained multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 27(4), 949\u2013963.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR141","volume-title":"Multicriteria optimization","author":"M Ehrgott","year":"2005","unstructured":"Ehrgott, M. (2005). Multicriteria optimization. Springer."},{"key":"172_CR142","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1162\/106365601750190398","volume":"9","author":"N Hansen","year":"2001","unstructured":"Hansen, N., & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9, 159\u2013195.","journal-title":"Evolutionary Computation"},{"issue":"1","key":"172_CR143","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/TEVC.2017.2672689","volume":"22","author":"P Yang","year":"2018","unstructured":"Yang, P., Tang, K., & Yao, X. (2018). Turning high-dimensional optimization into computationally expensive optimization. IEEE Transactions on Evolutionary Computation, 22(1), 143\u2013156.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"5","key":"172_CR144","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1109\/TEVC.2022.3199045","volume":"27","author":"W Liu","year":"2023","unstructured":"Liu, W., Wang, R., Zhang, T., Li, K., Li, W., Ishibuchi, H., & Liao, X. (2023). Hybridization of evolutionary algorithm and deep reinforcement learning for multiobjective orienteering optimization. IEEE Transactions on Evolutionary Computation, 27(5), 1260\u20131274.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR145","unstructured":"Chen, X., & Tian, Y. (2019). Learning to perform local rewriting for combinatorial optimization. In Advances in neural information processing systems (Vol. 32)."},{"issue":"6","key":"172_CR146","doi-asserted-by":"publisher","first-page":"1794","DOI":"10.1109\/TEVC.2022.3232776","volume":"27","author":"Z Zhan","year":"2023","unstructured":"Zhan, Z., Li, J., Kwong, S., & Zhang, J. (2023). Learning-aided evolution for optimization. IEEE Transactions on Evolutionary Computation, 27(6), 1794\u20131808.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"1","key":"172_CR147","doi-asserted-by":"publisher","first-page":"111","DOI":"10.23919\/cje.2022.00.100","volume":"32","author":"Y Tian","year":"2023","unstructured":"Tian, Y., Zhang, X., He, C., Tan, K. C., & Jin, Y. (2023). Principled design of translation, scale, and rotation invariant variation operators for metaheuristics. Chinese Journal of Electronics, 32(1), 111\u2013129.","journal-title":"Chinese Journal of Electronics"},{"issue":"1","key":"172_CR148","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1109\/TITS.2023.3313688","volume":"25","author":"Z Zheng","year":"2024","unstructured":"Zheng, Z., Yao, S., Li, G., Han, L., & Wang, Z. (2024). Pareto improver: Learning improvement heuristics for multi-objective route planning. IEEE Transactions on Intelligent Transportation Systems, 25(1), 1033\u20131043.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"2","key":"172_CR149","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1109\/6.819926","volume":"37","author":"DB Fogel","year":"2000","unstructured":"Fogel, D. B. (2000). What is evolutionary computation? IEEE Spectrum, 37(2), 26\u201332.","journal-title":"IEEE Spectrum"},{"issue":"3","key":"172_CR150","first-page":"408","volume":"21","author":"H-L Liu","year":"2016","unstructured":"Liu, H.-L., Chen, L., Deb, K., & Goodman, E. D. (2016). Investigating the effect of imbalance between convergence and diversity in evolutionary multiobjective algorithms. IEEE Transactions on Evolutionary Computation, 21(3), 408\u2013425.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"172_CR151","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2480741.2480752","volume":"45","author":"M \u010crepin\u0161ek","year":"2013","unstructured":"\u010crepin\u0161ek, M., Liu, S.-H., & Mernik, M. (2013). Exploration and exploitation in evolutionary algorithms: A survey. ACM Computing Surveys (CSUR), 45(3), 1\u201333.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"172_CR152","doi-asserted-by":"crossref","unstructured":"Collins, T. D. (1998). Understanding evolutionary computing: A hands on approach. In Proceedings of the IEEE conference on evolutionary computation (pp. 564\u2013 569). IEEE.","DOI":"10.1109\/ICEC.1998.700090"},{"issue":"1","key":"172_CR153","first-page":"661","volume":"30","author":"Y Huang","year":"2024","unstructured":"Huang, Y., Zhang, Z., Jiao, A., Ma, Y., & Cheng, R. (2024). A comparative visual analytics framework for evaluating evolutionary processes in multi-objective optimization. IEEE Transactions on Visualization and Computer Graphics, 30(1), 661\u2013671.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"issue":"2","key":"172_CR154","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TEVC.2010.2083668","volume":"15","author":"H Someya","year":"2011","unstructured":"Someya, H. (2011). Theoretical analysis of phenotypic diversity in real-valued evolutionary algorithms with more-than-one-element replacement. IEEE Transactions on Evolutionary Computation, 15(2), 248\u2013266.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"172_CR155","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/s00453-010-9472-3","volume":"59","author":"B Doerr","year":"2011","unstructured":"Doerr, B., & Jansen, T. (2011). Theory of evolutionary computation. Algorithmica, 59(3), 299\u2013300.","journal-title":"Algorithmica"},{"key":"172_CR156","unstructured":"Li, Y., Chen, L., Liu, A., Yu, K., & Wen, L. (2024). Chatcite: LLM agent with human workflow guidance for comparative literature summary. arXiv:2403.02574"},{"key":"172_CR157","doi-asserted-by":"crossref","unstructured":"Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., & Myers, B. (2024). Using an LLM to help with code understanding. In Proceedings of the IEEE\/ACM 46th international conference on software engineering (pp. 1\u201313).","DOI":"10.1145\/3597503.3639187"},{"key":"172_CR158","doi-asserted-by":"crossref","unstructured":"Yu, X., Chen, Z., Ling, Y., Dong, S., Liu, Z., & Lu, Y. (2023). Temporal data meets LLM\u2013explainable financial time series forecasting. arXiv:2306.11025","DOI":"10.18653\/v1\/2023.emnlp-industry.69"},{"key":"172_CR159","unstructured":"Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., et al. (2023). Time-LLM: Time series forecasting by reprogramming large language models. arXiv:2310.01728"},{"issue":"2","key":"172_CR160","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182\u2013197.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"4","key":"172_CR161","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/MCI.2017.2742868","volume":"12","author":"Y Tian","year":"2017","unstructured":"Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Computational Intelligence Magazine, 12(4), 73\u201387.","journal-title":"IEEE Computational Intelligence Magazine"},{"issue":"2","key":"172_CR162","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/4235.771163","volume":"3","author":"X Yao","year":"1999","unstructured":"Yao, X., Liu, Y., & Lin, G. (1999). Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), 82\u2013102.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"172_CR163","doi-asserted-by":"crossref","unstructured":"Zamfirescu-Pereira, J., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny can\u2019t prompt: How non-ai experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1\u2013 21).","DOI":"10.1145\/3544548.3581388"},{"key":"172_CR164","unstructured":"Dong, Q., Li, L., Dai, D., Zheng, C., Wu, Z., Chang, B., Sun, X., Xu, J., & Sui, Z. (2022). A survey on in-context learning. arXiv: 2301.00234"},{"key":"172_CR165","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824\u201324837.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"172_CR166","unstructured":"Zhou, Z., Ning, X., Hong, K., Fu, T., Xu, J., Li, S., Lou, Y., Wang, L., Yuan, Z., Li, X., et al. (2024). A survey on efficient inference for large language models. arXiv:2404.14294"},{"key":"172_CR167","doi-asserted-by":"crossref","unstructured":"Wang, J., He, C., Li, R., Chen, H., Zhai, C., & Zhang, M. (2021). Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework. Physics of Fluids, 33(8).","DOI":"10.1063\/5.0053979"},{"issue":"3","key":"172_CR168","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.cja.2021.03.006","volume":"35","author":"W Jing","year":"2022","unstructured":"Jing, W., Runze, L., Cheng, H., Haixin, C., Cheng, R., Chen, Z., & Zhang, M. (2022). An inverse design method for supercritical airfoil based on conditional generative models. Chinese Journal of Aeronautics, 35(3), 62\u201374.","journal-title":"Chinese Journal of Aeronautics"},{"key":"172_CR169","unstructured":"Liu, Z., Xu, Y., Xu, Y., Qian, Q., Li, H., Ji, X., ... & Jin, R. (2022). Improved fine-tuning by better leveraging pre-training data.A dvances in Neural Information Processing Systems, 35, 32568\u201332581."}],"container-title":["Journal of Membrane Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00172-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41965-024-00172-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00172-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T19:02:40Z","timestamp":1750359760000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41965-024-00172-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,12]]},"references-count":169,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["172"],"URL":"https:\/\/doi.org\/10.1007\/s41965-024-00172-x","relation":{},"ISSN":["2523-8906","2523-8914"],"issn-type":[{"value":"2523-8906","type":"print"},{"value":"2523-8914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,12]]},"assertion":[{"value":"16 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}