{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:40:41Z","timestamp":1775832041465,"version":"3.50.1"},"reference-count":132,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T00:00:00Z","timestamp":1715990400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T00:00:00Z","timestamp":1715990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376202"],"award-info":[{"award-number":["62376202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>As potent approaches for addressing computationally expensive optimization problems, surrogate-assisted evolutionary algorithms (SAEAs) have garnered increasing attention. Prevailing endeavors in evolutionary computation predominantly concentrate on expensive continuous optimization problems, with a notable scarcity of investigations directed toward expensive combinatorial optimization problems (ECOPs). Nevertheless, numerous ECOPs persist in practical applications. The widespread prevalence of such problems starkly contrasts the limited development of relevant research. Motivated by this disparity, this paper conducts a comprehensive survey on SAEAs tailored to address ECOPs. This survey comprises two primary segments. The first segment synthesizes prevalent global, local, hybrid, and learning search strategies, elucidating their respective strengths and weaknesses. Subsequently, the second segment furnishes an overview of surrogate-based evaluation technologies, delving into three pivotal facets: model selection, construction, and management. The paper also discusses several potential future directions for SAEAs with a focus towards expensive combinatorial optimization.<\/jats:p>","DOI":"10.1007\/s40747-024-01465-5","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T07:01:30Z","timestamp":1716015690000},"page":"5933-5949","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Surrogate-assisted evolutionary algorithms for expensive combinatorial optimization: a survey"],"prefix":"10.1007","volume":"10","author":[{"given":"Shulei","family":"Liu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4805-3780","authenticated-orcid":false,"given":"Handing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,18]]},"reference":[{"issue":"3","key":"1465_CR1","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1145\/937503.937505","volume":"35","author":"C Blum","year":"2003","unstructured":"Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268\u2013308","journal-title":"ACM Comput Surv"},{"issue":"2","key":"1465_CR2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TEVC.2020.3040272","volume":"25","author":"H Zhang","year":"2021","unstructured":"Zhang H, Jin Y, Cheng R, Hao K (2021) Efficient evolutionary search of attention convolutional networks via sampled training and node inheritance. IEEE Trans Evol Comput 25(2):371\u2013385","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR3","unstructured":"Jian S-J, Hsieh S-Y (2022) A niching regression adaptive memetic algorithm for multimodal optimization of the euclidean traveling salesman problem. IEEE Trans Evol Comput pp 1\u20131"},{"issue":"5","key":"1465_CR4","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/TEVC.2022.3149601","volume":"26","author":"S Liu","year":"2022","unstructured":"Liu S, Wang H, Peng W, Yao W (2022) A surrogate-assisted evolutionary feature selection algorithm with parallel random grouping for high-dimensional classification. IEEE Trans Evol Comput 26(5):1087\u20131101","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR5","first-page":"2201","volume":"34","author":"J Bhatia","year":"2021","unstructured":"Bhatia J, Jackson H, Tian Y, Jie X, Matusik W (2021) Evolution gym: A large-scale benchmark for evolving soft robots. Adv Neural Inf Process Syst 34:2201\u20132214","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"1465_CR6","doi-asserted-by":"crossref","first-page":"5721","DOI":"10.1038\/s41467-021-25874-z","volume":"12","author":"A Gupta","year":"2021","unstructured":"Gupta A, Savarese S, Ganguli S, Fei-Fei L (2021) Embodied intelligence via learning and evolution. Nat Commun 12(1):5721","journal-title":"Nat Commun"},{"issue":"4","key":"1465_CR7","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TEVC.2019.2950935","volume":"24","author":"S Wang","year":"2019","unstructured":"Wang S, Liu J, Jin Y (2019) Surrogate-assisted robust optimization of large-scale networks based on graph embedding. IEEE Trans Evol Comput 24(4):735\u2013749","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR8","first-page":"21453","volume-title":"Advances in neural information processing systems","author":"W Runzhong","year":"2021","unstructured":"Runzhong W, Zhigang H, Gan L, Jiayi Z, Junchi Y, Feng Q, Shuang Y, Jun Z, Xiaokang Y (2021) A bi-level framework for learning to solve combinatorial optimization on graphs. In: Ranzato M, Beygelzimer A, Dauphin Y, Liang PS, Wortman Vaughan J (eds) Advances in neural information processing systems, vol 34. Curran Associates Inc, pp 21453\u201321466"},{"issue":"6","key":"1465_CR9","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1109\/TEVC.2013.2281527","volume":"17","author":"NR Sabar","year":"2013","unstructured":"Sabar NR, Ayob M, Kendall G, Rong Q (2013) Grammatical evolution hyper-heuristic for combinatorial optimization problems. IEEE Trans Evol Comput 17(6):840\u2013861","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"1465_CR10","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TEVC.2022.3144675","volume":"26","author":"Y Tian","year":"2022","unstructured":"Tian Y, Feng Y, Wang C, Cao R, Zhang X, Pei X, Tan KC, Jin Y (2022) A large-scale combinatorial many-objective evolutionary algorithm for intensity-modulated radiotherapy planning. IEEE Trans Evol Comput 26(6):1511\u20131525","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"1465_CR11","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/TEVC.2021.3123960","volume":"26","author":"W Lan","year":"2021","unstructured":"Lan W, Ye Z, Ruan P, Liu J, Yang P, Yao X (2021) Region-focused memetic algorithms with smart initialization for real-world large-scale waste collection problems. IEEE Trans Evol Comput 26(4):704\u2013718","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR12","doi-asserted-by":"crossref","unstructured":"Honglin Z, Yaohua W, Jinchang H, Yanyan W (2023) Collaborative optimization of task scheduling and multi-agent path planning in automated warehouses. Complex Intell Syst pp 1\u201312","DOI":"10.1007\/s40747-023-01023-5"},{"issue":"9","key":"1465_CR13","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/TCYB.2018.2849403","volume":"49","author":"X Cai","year":"2019","unstructured":"Cai X, Sun H, Zhang Q, Huang Y (2019) A grid weighted sum pareto local search for combinatorial multi and many-objective optimization. IEEE Trans Cybern 49(9):3586\u20133598","journal-title":"IEEE Trans Cybern"},{"issue":"7","key":"1465_CR14","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TCYB.2017.2728120","volume":"48","author":"Yu Xue","year":"2018","unstructured":"Xue Yu, Chen W-N, Tianlong G, Zhang H, Yuan H, Kwong S, Zhang J (2018) Set-based discrete particle swarm optimization based on decomposition for permutation-based multiobjective combinatorial optimization problems. IEEE Trans Cybern 48(7):2139\u20132153","journal-title":"IEEE Trans Cybern"},{"key":"1465_CR15","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10898-014-0213-z","volume":"63","author":"D Abraham","year":"2015","unstructured":"Abraham D, Juan JP, Eduardo GP, Nenad M (2015) Multi-objective variable neighborhood search: an application to combinatorial optimization problems. J Global Optim 63:515\u2013536","journal-title":"J Global Optim"},{"issue":"3","key":"1465_CR16","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/TEVC.2018.2869001","volume":"23","author":"Y Jin","year":"2019","unstructured":"Jin Y, Wang H, Chugh T, Guo D, Miettinen K (2019) Data-driven evolutionary optimization: an overview and case studies. IEEE Trans Evol Comput 23(3):442\u2013458","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"1465_CR17","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1109\/TEVC.2016.2555315","volume":"20","author":"H Wang","year":"2016","unstructured":"Wang H, Jin Y, Jansen JO (2016) Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system. IEEE Trans Evol Comput 20(6):939\u2013952","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"1465_CR18","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1109\/TCYB.2018.2869674","volume":"50","author":"H Wang","year":"2020","unstructured":"Wang H, Jin Y (2020) A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems. IEEE Trans Cybern 50(2):536\u2013549","journal-title":"IEEE Trans Cybern"},{"key":"1465_CR19","doi-asserted-by":"crossref","unstructured":"Lin J, Gebbran D, Dragi\u010devi\u0107 T (2023) Surrogate-assisted combinatorial optimization of ev fast charging stations. IEEE Trans Transp Electr pp 1\u20131","DOI":"10.1109\/TTE.2023.3266550"},{"key":"1465_CR20","doi-asserted-by":"crossref","unstructured":"Lepr\u00eatre F, Fonlupt C, Verel S, Marion V (2020) Combinatorial surrogate-assisted optimization for bus stops spacing problem. In: Artificial evolution: 14th international conference, \u00c9volution artificielle, EA 2019, Mulhouse, France, October 29\u201330, 2019, Revised Selected Papers 14, Springer, pp 42\u201352","DOI":"10.1007\/978-3-030-45715-0_4"},{"issue":"2","key":"1465_CR21","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/TEVC.2019.2924461","volume":"24","author":"Y Sun","year":"2019","unstructured":"Sun Y, Wang H, Xue B, Jin Y, Yen GG, Zhang M (2019) Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor. IEEE Trans Evol Comput 24(2):350\u2013364","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"1465_CR22","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","volume":"1","author":"Y Jin","year":"2011","unstructured":"Jin Y (2011) Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol Comput 1(2):61\u201370","journal-title":"Swarm Evol Comput"},{"key":"1465_CR23","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.asoc.2017.01.039","volume":"55","author":"T Bartz-Beielstein","year":"2017","unstructured":"Bartz-Beielstein T, Zaefferer M (2017) Model-based methods for continuous and discrete global optimization. Appl Soft Comput 55:154\u2013167","journal-title":"Appl Soft Comput"},{"key":"1465_CR24","doi-asserted-by":"crossref","unstructured":"Liu S, Wang H, Yao W, Peng W (2023) Surrogate-assisted environmental selection for fast hypervolume-based many-objective optimization. IEEE Trans Evol Comput pp 1\u20131","DOI":"10.1109\/TEVC.2023.3243632"},{"key":"1465_CR25","doi-asserted-by":"crossref","unstructured":"Fan L, Wang H (2022) Surrogate-assisted evolutionary neural architecture search with network embedding. Complex Intell Syst, pp 1\u201319","DOI":"10.1007\/s40747-022-00929-w"},{"issue":"2","key":"1465_CR26","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1109\/TEVC.2013.2248012","volume":"18","author":"B Liu","year":"2013","unstructured":"Liu B, Zhang Q, Gielen GGE (2013) A gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems. IEEE Trans Evol Comput 18(2):180\u2013192","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"1465_CR27","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1109\/TEVC.2021.3073648","volume":"25","author":"Z Song","year":"2021","unstructured":"Song Z, Wang H, He C, Jin Y (2021) A kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. IEEE Trans Evol Comput 25(6):1013\u20131027","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR28","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s40747-021-00277-1","volume":"7","author":"Z Ren","year":"2021","unstructured":"Ren Z, Sun C, Tan Y, Zhang G, Qin S (2021) A bi-stage surrogate-assisted hybrid algorithm for expensive optimization problems. Complex Intell Syst 7:1391\u20131405","journal-title":"Complex Intell Syst"},{"issue":"4","key":"1465_CR29","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TEVC.2021.3065707","volume":"25","author":"F Zhang","year":"2021","unstructured":"Zhang F, Yi Mei S, Nguyen MZ, Tan KC (2021) Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Trans Evol Comput 25(4):651\u2013665","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"1465_CR30","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TEVC.2017.2675628","volume":"21","author":"C Sun","year":"2017","unstructured":"Sun C, Jin Y, Cheng R, Ding J, Zeng J (2017) Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Trans Evol Comput 21(4):644\u2013660","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"1465_CR31","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TEVC.2019.2919762","volume":"24","author":"X Cai","year":"2020","unstructured":"Cai X, Gao L, Li X (2020) Efficient generalized surrogate-assisted evolutionary algorithm for high-dimensional expensive problems. IEEE Trans Evol Comput 24(2):365\u2013379","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR32","unstructured":"Gu H, Wang H, Jin Y (2022) Surrogate-assisted differential evolution with adaptive multi-subspace search for large-scale expensive optimization. IEEE Trans Evol Comput, pp 1\u20131"},{"issue":"5","key":"1465_CR33","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1109\/TEVC.2021.3120980","volume":"26","author":"R Jiao","year":"2022","unstructured":"Jiao R, Xue B, Zhang M (2022) Investigating the correlation amongst the objective and constraints in gaussian process-assisted highly constrained expensive optimization. IEEE Trans Evol Comput 26(5):872\u2013885","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"1465_CR34","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TEVC.2022.3177936","volume":"27","author":"F-F Wei","year":"2023","unstructured":"Wei F-F, Chen W-N, Li Q, Jeon S-W, Zhang J (2023) Distributed and expensive evolutionary constrained optimization with on-demand evaluation. IEEE Trans Evol Comput 27(3):671\u2013685","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"1465_CR35","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TEVC.2023.3237605","volume":"27","author":"S Qin","year":"2023","unstructured":"Qin S, Sun C, Liu Q, Jin Y (2023) A performance indicator-based infill criterion for expensive multi-\/many-objective optimization. IEEE Trans Evol Comput 27(4):1085\u20131099","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR36","doi-asserted-by":"crossref","unstructured":"Song Z, Wang H, Xue B, Zhang M, Jin Y (2023) Balancing objective optimization and constraint satisfaction in expensive constrained evolutionary multi-objective optimization. IEEE Trans Evol Comput, pp 1\u20131","DOI":"10.1109\/TEVC.2023.3300181"},{"key":"1465_CR37","doi-asserted-by":"crossref","unstructured":"Stork J, Friese M, Zaefferer M, Bartz-Beielstein T, Fischbach A, Breiderhoff B, Naujoks B, Tu\u0161ar T (2020) Open issues in surrogate-assisted optimization. In: High-performance simulation-based optimization, pp 225\u2013244","DOI":"10.1007\/978-3-030-18764-4_10"},{"issue":"5","key":"1465_CR38","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TEVC.2021.3130838","volume":"26","author":"MN Omidvar","year":"2022","unstructured":"Omidvar MN, Li X, Yao X (2022) A review of population-based metaheuristics for large-scale black-box global optimization-part i. IEEE Trans Evol Comput 26(5):802\u2013822","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"1465_CR39","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1109\/TEVC.2021.3130835","volume":"26","author":"MN Omidvar","year":"2022","unstructured":"Omidvar MN, Li X, Yao X (2022) A review of population-based metaheuristics for large-scale black-box global optimization-part ii. IEEE Trans Evol Comput 26(5):823\u2013843","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR40","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110061","volume":"136","author":"B Derbel","year":"2023","unstructured":"Derbel B, Pruvost G, Liefooghe A, Verel S, Zhang Q (2023) Walsh-based surrogate-assisted multi-objective combinatorial optimization: a fine-grained analysis for pseudo-Boolean functions. Appl Soft Comput 136:110061","journal-title":"Appl Soft Comput"},{"key":"1465_CR41","doi-asserted-by":"crossref","unstructured":"Pruvost G, Derbel B, Liefooghe A, Verel S, Zhang Q (2020) Surrogate-assisted multi-objective combinatorial optimization based on decomposition and walsh basis. In Proceedings of the 2020 genetic and evolutionary computation conference, pp 542\u2013550","DOI":"10.1145\/3377930.3390149"},{"key":"1465_CR42","doi-asserted-by":"crossref","unstructured":"Gu Q, Wang Q, Xiong NN, Jiang S, Chen L (2021) Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems. Complex Intell Syst, pp 1\u201320","DOI":"10.1007\/s40747-020-00249-x"},{"key":"1465_CR43","volume":"159","author":"G Qinghua","year":"2021","unstructured":"Qinghua G, Wang D, Jiang S, Xiong N, Jin Yu (2021) An improved assisted evolutionary algorithm for data-driven mixed integer optimization based on two_arch. Comput Ind Eng 159:107463","journal-title":"Comput Ind Eng"},{"key":"1465_CR44","doi-asserted-by":"crossref","unstructured":"Prado RS, Silva RCP, Guimar\u00e3es FG, Neto OM (2010) Using differential evolution for combinatorial optimization: a general approach. In: 2010 IEEE international conference on systems, man and cybernetics, pp 11\u201318","DOI":"10.1109\/ICSMC.2010.5642193"},{"issue":"9","key":"1465_CR45","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1109\/TCYB.2016.2562674","volume":"47","author":"S Nguyen","year":"2017","unstructured":"Nguyen S, Zhang M, Tan KC (2017) Surrogate-assisted genetic programming with simplified models for automated design of dispatching rules. IEEE Trans Cybern 47(9):2951\u20132965","journal-title":"IEEE Trans Cybern"},{"key":"1465_CR46","unstructured":"Fan Q, Bi Y, Xue B, Zhang M (2022) A global and local surrogate-assisted genetic programming approach to image classification. IEEE Trans Evol Comput"},{"key":"1465_CR47","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.ins.2022.12.004","volume":"622","author":"R Espinosa","year":"2023","unstructured":"Espinosa R, Jim\u00e9nez F, Palma J (2023) Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data. Inf Sci 622:1064\u20131091","journal-title":"Inf Sci"},{"issue":"1","key":"1465_CR48","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TEVC.2021.3095261","volume":"26","author":"S Wang","year":"2022","unstructured":"Wang S, Mei Y, Zhang M, Yao X (2022) Genetic programming with niching for uncertain capacitated arc routing problem. IEEE Trans Evol Comput 26(1):73\u201387","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"1465_CR49","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue B, Zhang M, Browne WN, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606\u2013626","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR50","doi-asserted-by":"crossref","unstructured":"Jiao R, Nguyen BH, Xue B, Zhang M (2023) A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2023.3292527"},{"issue":"5","key":"1465_CR51","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1109\/TEVC.2021.3134804","volume":"26","author":"K Chen","year":"2021","unstructured":"Chen K, Xue B, Zhang M, Zhou F (2021) Correlation-guided updating strategy for feature selection in classification with surrogate-assisted particle swarm optimization. IEEE Trans Evol Comput 26(5):1015\u20131029","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"1465_CR52","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1109\/TSMCB.2012.2227469","volume":"43","author":"B Xue","year":"2013","unstructured":"Xue B, Zhang M, Browne WN (2013) Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans Cybern 43(6):1656\u20131671","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"1465_CR53","first-page":"1","volume":"13","author":"Yu Xue","year":"2019","unstructured":"Xue Yu, Xue B, Zhang M (2019) Self-adaptive particle swarm optimization for large-scale feature selection in classification. ACM Trans Knowl Discov Data (TKDD) 13(5):1\u201327","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"key":"1465_CR54","volume":"121","author":"H Pei","year":"2022","unstructured":"Pei H, Pan J-S, Chu S-C, Sun C (2022) Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection. Appl Soft Comput 121:108736","journal-title":"Appl Soft Comput"},{"key":"1465_CR55","unstructured":"Nguyen BH, Xue B, Zhang M (2022) A constrained competitive swarm optimiser with an svm-based surrogate model for feature selection. IEEE Trans Evol Comput"},{"key":"1465_CR56","doi-asserted-by":"crossref","unstructured":"Song X, Zhang Y, Gong D, Liu H, Zhang W (2022) Surrogate sample-assisted particle swarm optimization for feature selection on high-dimensional data. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2022.3175226"},{"key":"1465_CR57","doi-asserted-by":"crossref","unstructured":"Zheng X, Ji R, Tang L, Zhang B, Liu J, Tian Q (2019) Multinomial distribution learning for effective neural architecture search. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 1304\u20131313","DOI":"10.1109\/ICCV.2019.00139"},{"key":"1465_CR58","unstructured":"Li J-Y, Zhan Z-H, Xu J, Kwong S, Zhang J (2021) Surrogate-assisted hybrid-model estimation of distribution algorithm for mixed-variable hyperparameters optimization in convolutional neural networks. IEEE Trans Neural Netw Learn Syst"},{"key":"1465_CR59","doi-asserted-by":"crossref","unstructured":"Santucci V, Ceberio J (2023) Doubly stochastic matrix models for estimation of distribution algorithms. arXiv preprint arXiv:2304.02458","DOI":"10.1145\/3583131.3590371"},{"key":"1465_CR60","doi-asserted-by":"crossref","unstructured":"Irurozki E, L\u00f3pez-Ib\u00e1\u00f1ez M (2021) Unbalanced mallows models for optimizing expensive black-box permutation problems. In Proceedings of the genetic and evolutionary computation conference, pp 225\u2013233","DOI":"10.1145\/3449639.3459366"},{"issue":"4","key":"1465_CR61","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28\u201339","journal-title":"IEEE Comput Intell Mag"},{"issue":"2","key":"1465_CR62","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11721-015-0106-x","volume":"9","author":"LP C\u00e1ceres","year":"2015","unstructured":"C\u00e1ceres LP, L\u00f3pez-Ib\u00e1\u00f1ez M, St\u00fctzle T (2015) Ant colony optimization on a limited budget of evaluations. Swarm Intell 9(2):103\u2013124","journal-title":"Swarm Intell"},{"key":"1465_CR63","volume":"69","author":"S Dhananjay Thiruvady","year":"2022","unstructured":"Dhananjay Thiruvady S, Nguyen FS, Zaidi N, Li X (2022) Surrogate-assisted population based aco for resource constrained job scheduling with uncertainty. Swarm Evol Comput 69:101029","journal-title":"Swarm Evol Comput"},{"key":"1465_CR64","doi-asserted-by":"crossref","unstructured":"Alonso-Barba JI, Luis de\u00a0la O, Regnier-Coudert O, McCall J, G\u00e1mez JA, Puerta JM (2015) Ant colony and surrogate tree-structured models for orderings-based bayesian network learning. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation, pp 543\u2013550","DOI":"10.1145\/2739480.2754806"},{"key":"1465_CR65","volume":"116","author":"W Ma","year":"2021","unstructured":"Ma W, Zhou X, Zhu H, Li L, Jiao L (2021) A two-stage hybrid ant colony optimization for high-dimensional feature selection. Pattern Recogn 116:107933","journal-title":"Pattern Recogn"},{"key":"1465_CR66","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105285","volume":"192","author":"M Paniri","year":"2020","unstructured":"Paniri M, Dowlatshahi MB, Nezamabadi-Pour H (2020) Mlaco: A multi-label feature selection algorithm based on ant colony optimization. Knowl-Based Syst 192:105285","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"1465_CR67","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/TEVC.2013.2281531","volume":"18","author":"T Liao","year":"2014","unstructured":"Liao T, Socha K, Marco A, de Oca M, St\u00fctzle T, Dorigo M (2014) Ant colony optimization for mixed-variable optimization problems. IEEE Trans Evol Comput 18(4):503\u2013518","journal-title":"IEEE Trans Evol Comput"},{"issue":"11","key":"1465_CR68","doi-asserted-by":"crossref","first-page":"11348","DOI":"10.1109\/TCYB.2021.3064676","volume":"52","author":"J Liu","year":"2021","unstructured":"Liu J, Wang Y, Sun G, Pang T (2021) Multisurrogate-assisted ant colony optimization for expensive optimization problems with continuous and categorical variables. IEEE Trans Cybern 52(11):11348\u201311361","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"1465_CR69","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0377-2217(01)00104-7","volume":"137","author":"A Jaszkiewicz","year":"2002","unstructured":"Jaszkiewicz A (2002) Genetic local search for multi-objective combinatorial optimization. Eur J Oper Res 137(1):50\u201371","journal-title":"Eur J Oper Res"},{"issue":"6","key":"1465_CR70","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1007\/s10732-018-9381-1","volume":"24","author":"A Blot","year":"2018","unstructured":"Blot A, Kessaci M\u00c9, Jourdan L (2018) Survey and unification of local search techniques in metaheuristics for multi-objective combinatorial optimisation. J Heuristics 24(6):853\u2013877","journal-title":"J Heuristics"},{"issue":"9","key":"1465_CR71","doi-asserted-by":"crossref","first-page":"3586","DOI":"10.1109\/TCYB.2018.2849403","volume":"49","author":"X Cai","year":"2018","unstructured":"Cai X, Sun H, Zhang Q, Huang Y (2018) A grid weighted sum pareto local search for combinatorial multi and many-objective optimization. IEEE Trans Cybern 49(9):3586\u20133598","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"1465_CR72","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/S0377-2217(02)00768-3","volume":"149","author":"V Valls","year":"2003","unstructured":"Valls V, Quintanilla S, Ballestin F (2003) Resource-constrained project scheduling: a critical activity reordering heuristic. Eur J Oper Res 149(2):282\u2013301","journal-title":"Eur J Oper Res"},{"key":"1465_CR73","doi-asserted-by":"crossref","unstructured":"Katayama K, Hamamoto A, Narihisa H (2004) Solving the maximum clique problem by k-opt local search. In: Proceedings of the 2004 ACM symposium on applied computing, pp 1021\u20131025","DOI":"10.1145\/967900.968107"},{"key":"1465_CR74","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s12532-009-0004-6","volume":"1","author":"K Helsgaun","year":"2009","unstructured":"Helsgaun K (2009) General k-opt submoves for the lin-kernighan tsp heuristic. Math Program Comput 1:119\u2013163","journal-title":"Math Program Comput"},{"key":"1465_CR75","volume":"126","author":"S Liu","year":"2022","unstructured":"Liu S, Wang H, Yao W (2022) A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming. Appl Soft Comput 126:109263","journal-title":"Appl Soft Comput"},{"issue":"4","key":"1465_CR76","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1287\/trsc.1050.0135","volume":"40","author":"S Ropke","year":"2006","unstructured":"Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transport Sci 40(4):455\u2013472","journal-title":"Transport Sci"},{"key":"1465_CR77","doi-asserted-by":"crossref","unstructured":"Pisinger D, Ropke S (2019) Large neighborhood search. Handbook of metaheuristics, pp 99\u2013127","DOI":"10.1007\/978-3-319-91086-4_4"},{"key":"1465_CR78","volume":"146","author":"STW Mara","year":"2022","unstructured":"Mara STW, Norcahyo R, Jodiawan P, Lusiantoro L, Rifai AP (2022) A survey of adaptive large neighborhood search algorithms and applications. Comput Oper Res 146:105903","journal-title":"Comput Oper Res"},{"issue":"6","key":"1465_CR79","doi-asserted-by":"crossref","first-page":"4135","DOI":"10.1016\/j.asoc.2011.02.032","volume":"11","author":"C Blum","year":"2011","unstructured":"Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11(6):4135\u20134151","journal-title":"Appl Soft Comput"},{"key":"1465_CR80","volume":"93","author":"Y Han","year":"2020","unstructured":"Han Y, Li J, Sang H, Liu Y, Gao K, Pan Q (2020) Discrete evolutionary multi-objective optimization for energy-efficient blocking flow shop scheduling with setup time. Appl Soft Comput 93:106343","journal-title":"Appl Soft Comput"},{"key":"1465_CR81","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s40747-019-0113-4","volume":"6","author":"ACC Carlos","year":"2020","unstructured":"Carlos ACC, Silvia GB, Josu\u00e9 FG, Ma Guadalupe CT, Raquel HG (2020) Evolutionary multiobjective optimization: open research areas and some challenges lying ahead. Complex Intell Syst 6:221\u2013236","journal-title":"Complex Intell Syst"},{"key":"1465_CR82","unstructured":"Pruvost G, Derbel B, Liefooghe A, Verel S, Zhang Q (2021) A modular surrogate-assisted framework for expensive multiobjective combinatorial optimization"},{"key":"1465_CR83","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s12293-021-00326-9","volume":"13","author":"L Han","year":"2021","unstructured":"Han L, Wang H (2021) A random forest assisted evolutionary algorithm using competitive neighborhood search for expensive constrained combinatorial optimization. Memetic Comput 13:19\u201330","journal-title":"Memetic Comput"},{"key":"1465_CR84","doi-asserted-by":"crossref","unstructured":"de Moraes MB, Palermo Coelho G (2022) A diversity preservation method for expensive multi-objective combinatorial optimization problems using novel-first tabu search and moea\/d. Expert Syst Appl 202:117251","DOI":"10.1016\/j.eswa.2022.117251"},{"key":"1465_CR85","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s00500-006-0139-6","volume":"11","author":"J Tang","year":"2007","unstructured":"Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput 11:873\u2013888","journal-title":"Soft Comput"},{"key":"1465_CR86","doi-asserted-by":"crossref","unstructured":"De\u00a0Moraes MB, Coelho GP (2022) A random forest-assisted decomposition-based evolutionary algorithm for multi-objective combinatorial optimization problems. In: 2022 IEEE congress on evolutionary computation (CEC), pp 1\u20138","DOI":"10.1109\/CEC55065.2022.9870412"},{"key":"1465_CR87","doi-asserted-by":"crossref","unstructured":"Liu B, Sun N, Zhang Q, Grout V, Gielen G (2016) A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp 1650\u20131657","DOI":"10.1109\/CEC.2016.7743986"},{"key":"1465_CR88","unstructured":"Lin X, Yang Z, Zhang Q (2022) Pareto set learning for neural multi-objective combinatorial optimization. arXiv preprint arXiv:2203.15386"},{"key":"1465_CR89","first-page":"3773","volume":"34","author":"A Deshwal","year":"2020","unstructured":"Deshwal A, Belakaria S, Doppa JR, Fern A (2020) Optimizing discrete spaces via expensive evaluations: a learning to search framework. Proc AAAI Conf Artif Intell 34:3773\u20133780","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1465_CR90","first-page":"24912","volume":"34","author":"T Mundhenk","year":"2021","unstructured":"Mundhenk T, Landajuela M, Glatt R, Santiago CP, Petersen BK et al (2021) Symbolic regression via deep reinforcement learning enhanced genetic programming seeding. Adv Neural Inf Process Syst 34:24912\u201324923","journal-title":"Adv Neural Inf Process Syst"},{"issue":"3","key":"1465_CR91","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TEVC.2022.3177605","volume":"27","author":"H Zhen","year":"2023","unstructured":"Zhen H, Gong W, Wang L (2023) Evolutionary sampling agent for expensive problems. IEEE Trans Evol Comput 27(3):716\u2013727","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR92","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"1465_CR93","first-page":"11096","volume":"34","author":"Y Ma","year":"2021","unstructured":"Ma Y, Li J, Cao Z, Song W, Zhang L, Chen Z, Tang J (2021) Learning to iteratively solve routing problems with dual-aspect collaborative transformer. Adv Neural Inf Process Syst 34:11096\u201311107","journal-title":"Adv Neural Inf Process Syst"},{"key":"1465_CR94","unstructured":"Khalil E, Dai H, Zhang Y, Dilkina B, Song L (2017) Learning combinatorial optimization algorithms over graphs. Adv Neural Inf Process Syst 30"},{"issue":"4","key":"1465_CR95","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s42256-022-00468-6","volume":"4","author":"MJA Schuetz","year":"2022","unstructured":"Schuetz MJA, Kyle Brubaker J, Katzgraber HG (2022) Combinatorial optimization with physics-inspired graph neural networks. Nat Mach Intell 4(4):367\u2013377","journal-title":"Nat Mach Intell"},{"issue":"3","key":"1465_CR96","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MCI.2023.3277768","volume":"18","author":"Yu Shengcai Liu","year":"2023","unstructured":"Shengcai Liu Yu, Zhang KT, Yao X (2023) How good is neural combinatorial optimization? a systematic evaluation on the traveling salesman problem. IEEE Comput Intell Mag 18(3):14\u201328","journal-title":"IEEE Comput Intell Mag"},{"issue":"1","key":"1465_CR97","volume":"134","author":"N Mazyavkina","year":"2021","unstructured":"Mazyavkina N, Sviridov S, Ivanov S, Burnaev E (2021) Reinforcement learning for combinatorial optimization: A survey. Comput Oper Res 134(1):105400","journal-title":"Comput Oper Res"},{"key":"1465_CR98","volume":"233","author":"Q Wang","year":"2021","unstructured":"Wang Q, Tang C (2021) Deep reinforcement learning for transportation network combinatorial optimization: A survey. Knowl-Based Syst 233:107526","journal-title":"Knowl-Based Syst"},{"key":"1465_CR99","unstructured":"Zaefferer M (2018) Surrogate models for discrete optimization problems"},{"key":"1465_CR100","doi-asserted-by":"crossref","unstructured":"Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley John & Sons","DOI":"10.1002\/9780470770801"},{"key":"1465_CR101","doi-asserted-by":"crossref","unstructured":"Liao T, Wang G, Yang B, Lee R, Pister K, Levine S, Calandra R (2019) Data-efficient learning of morphology and controller for a microrobot. In: 2019 International Conference on Robotics and Automation (ICRA)","DOI":"10.1109\/ICRA.2019.8793802"},{"key":"1465_CR102","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.jconhyd.2018.11.005","volume":"220","author":"JY Guo","year":"2019","unstructured":"Guo JY, Lu WX, Yang QC, Miao TS (2019) The application of 0\u20131 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. J Contam Hydrol 220:18\u201325","journal-title":"J Contam Hydrol"},{"key":"1465_CR103","doi-asserted-by":"crossref","unstructured":"Zhang J, Yao X, Liu M, Wang Y (2019) A bayesian discrete optimization algorithm for permutation based combinatorial problems. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp 874\u2013881","DOI":"10.1109\/SSCI44817.2019.9002675"},{"key":"1465_CR104","unstructured":"Oh C, Tomczak J, Gavves E, Welling M (2019) Combinatorial bayesian optimization using the graph cartesian product. Adv Neural Inf Process Syst 32"},{"key":"1465_CR105","unstructured":"Oh C, Tomczak J, Gavves E, Welling M (2019) Combo: Combinatorial bayesian optimization using graph representations. In: ICML Workshop on Learning and Reasoning with Graph-Structured Data"},{"issue":"1","key":"1465_CR106","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/TEVC.2022.3227562","volume":"27","author":"Y Peng","year":"2022","unstructured":"Peng Y, Song A, Ciesielski V, Fayek HM, Chang X (2022) Pre-nas: Evolutionary neural architecture search with predictor. IEEE Trans Evol Comput 27(1):26\u201336","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR107","doi-asserted-by":"crossref","unstructured":"Zhang F, Mei Y, Nguyen S, Zhang M (2023) Survey on genetic programming and machine learning techniques for heuristic design in job shop scheduling. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2023.3255246"},{"key":"1465_CR108","doi-asserted-by":"crossref","unstructured":"Xu M, Zhang F, Mei Y, Zhang M (2021) Genetic programming with archive for dynamic flexible job shop scheduling. In: 2021 IEEE congress on evolutionary computation (CEC), pp 2117\u20132124","DOI":"10.1109\/CEC45853.2021.9504752"},{"key":"1465_CR109","volume":"68","author":"Yu Mingyuan","year":"2022","unstructured":"Mingyuan Yu, Liang J, Zhao K, Zhou W (2022) An arbf surrogate-assisted neighborhood field optimizer for expensive problems. Swarm Evol Comput 68:100972","journal-title":"Swarm Evol Comput"},{"key":"1465_CR110","unstructured":"Li R, Emmerich MTM, Eggermont J, Bovenkamp EGP, Back T, Dijkstra J, Reiber JHC (2008) Metamodel-assisted mixed integer evolution strategies and their application to intravascular ultrasound image analysis. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp 2764\u20132771"},{"key":"1465_CR111","doi-asserted-by":"crossref","unstructured":"Zaefferer M, Stork J, Bartz-Beielstein T (2014) Distance measures for permutations in combinatorial efficient global optimization. In: Parallel problem solving from nature\u2013PPSN XIII: 13th international conference, Ljubljana, Slovenia, September 13\u201317, 2014. Proceedings 13, Springer, pp 373\u2013383","DOI":"10.1007\/978-3-319-10762-2_37"},{"key":"1465_CR112","doi-asserted-by":"crossref","unstructured":"Moraglio A, Kattan A (2011) Geometric generalisation of surrogate model based optimisation to combinatorial spaces. In: European conference on evolutionary computation in combinatorial optimization, Springer, pp 142\u2013154","DOI":"10.1007\/978-3-642-20364-0_13"},{"key":"1465_CR113","doi-asserted-by":"crossref","unstructured":"Hugo W, Pinaya L, Vieira S, Garcia-Dias R, Mechelli A (2020) Chapter 11 - autoencoders. In: Andrea M, Sandra V (eds) Machine learning, Academic Press, pp 193\u2013208","DOI":"10.1016\/B978-0-12-815739-8.00011-0"},{"key":"1465_CR114","doi-asserted-by":"crossref","unstructured":"Yuan G, Wang B, Xue B, Zhang M (2023) Particle swarm optimization for efficiently evolving deep convolutional neural networks using an autoencoder-based encoding strategy. IEEE Trans Evol Comput","DOI":"10.1109\/TEVC.2023.3245322"},{"issue":"4","key":"1465_CR115","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TEVC.2019.2950935","volume":"24","author":"S Wang","year":"2020","unstructured":"Wang S, Liu J, Jin Y (2020) Surrogate-assisted robust optimization of large-scale networks based on graph embedding. IEEE Trans Evol Comput 24(4):735\u2013749","journal-title":"IEEE Trans Evol Comput"},{"key":"1465_CR116","unstructured":"Luo R, Tian F, Qin T, Chen E, Liu T-Y (2018) Neural architecture optimization. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Advances in neural information processing systems, vol 31. Curran Associates, Inc"},{"issue":"9","key":"1465_CR117","doi-asserted-by":"crossref","first-page":"2561","DOI":"10.1080\/00207543.2019.1620362","volume":"58","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Yang J, Huang Z (2020) Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming. Int J Prod Res 58(9):2561\u20132580","journal-title":"Int J Prod Res"},{"key":"1465_CR118","doi-asserted-by":"crossref","unstructured":"Zhang F, Mei Y, Zhang M (2018) Surrogate-assisted genetic programming for dynamic flexible job shop scheduling. In: AI 2018: Advances in Artificial Intelligence: 31st Australasian Joint Conference, Wellington, New Zealand, December 11\u201314, 2018, Proceedings 31, Springer, pp 766\u2013772","DOI":"10.1007\/978-3-030-03991-2_69"},{"key":"1465_CR119","doi-asserted-by":"crossref","unstructured":"Tenne Y, Izui K, Nishiwaki S (2011) A classifier-assisted framework for expensive optimization problems: A knowledge-mining approach. In: Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers 5, Springer, pp 161\u2013175","DOI":"10.1007\/978-3-642-25566-3_12"},{"key":"1465_CR120","doi-asserted-by":"crossref","unstructured":"Bagheri S, Konen W, B\u00e4ck T (2016) Online selection of surrogate models for constrained black-box optimization. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1\u20138","DOI":"10.1109\/SSCI.2016.7850206"},{"key":"1465_CR121","doi-asserted-by":"crossref","unstructured":"Huang Q, De\u00a0Winter R, Van\u00a0Stein B, B\u00e4ck T, Kononova AV (2022) Multi-surrogate assisted efficient global optimization for discrete problems. In: 2022 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1650\u20131658","DOI":"10.1109\/SSCI51031.2022.10022132"},{"key":"1465_CR122","unstructured":"Feurer M, Klein A, Eggensperger K, Springenberg J, Blum M, Hutter F (2015) Efficient and robust automated machine learning. Adv Neural Inf Process Syst 28"},{"issue":"8","key":"1465_CR123","doi-asserted-by":"crossref","first-page":"8142","DOI":"10.1109\/TCYB.2021.3050141","volume":"52","author":"F Zhang","year":"2022","unstructured":"Zhang F, Yi M, Nguyen S, Mengjie Z (2022) Collaborative multifidelity-based surrogate models for genetic programming in dynamic flexible job shop scheduling. IEEE Trans Cybern 52(8):8142\u20138156","journal-title":"IEEE Trans Cybern"},{"key":"1465_CR124","doi-asserted-by":"crossref","unstructured":"Sun J, Yao W, Jiang T, Chen X (2023) Efficient search of comprehensively robust neural architectures via multi-fidelity evaluation. arXiv preprint arXiv:2305.07308","DOI":"10.2139\/ssrn.4458245"},{"key":"1465_CR125","unstructured":"Baptista R, Poloczek M (2018) Bayesian optimization of combinatorial structures. In: International conference on machine learning, pp 462\u2013471"},{"key":"1465_CR126","doi-asserted-by":"crossref","unstructured":"Beaucaire P, Beauthier C, Sainvitu C (2019) Multi-point infill sampling strategies exploiting multiple surrogate models. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp 1559\u20131567","DOI":"10.1145\/3319619.3328527"},{"issue":"16","key":"1465_CR127","doi-asserted-by":"crossref","first-page":"6683","DOI":"10.1002\/rnc.5131","volume":"30","author":"V Stojanovic","year":"2020","unstructured":"Stojanovic V, He S, Zhang B (2020) State and parameter joint estimation of linear stochastic systems in presence of faults and non-gaussian noises. Int J Robust Nonlinear Control 30(16):6683\u20136700","journal-title":"Int J Robust Nonlinear Control"},{"key":"1465_CR128","doi-asserted-by":"crossref","unstructured":"Behmanesh R, Rahimi I, Gandomi AH (2021) Evolutionary many-objective algorithms for combinatorial optimization problems: a comparative study. Arch Comput Methods Eng 28(2):673\u2013688","DOI":"10.1007\/s11831-020-09415-3"},{"key":"1465_CR129","doi-asserted-by":"crossref","unstructured":"Liu S, Yao W, Wang H, Peng W, Yang Y(2023) Rapidly evolving soft robots via action inheritance. IEEE Trans Evol Comput, pp 1\u20131","DOI":"10.1109\/TEVC.2023.3327459"},{"key":"1465_CR130","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/978-3-031-27250-9_15","volume-title":"Evolutionary multi-criterion optimization","author":"H Hao","year":"2023","unstructured":"Hao H, Zhou A (2023) A relation surrogate model for expensive multiobjective continuous and combinatorial optimization. In: Michael E, Deutz A, Wang H, Kononova AV, Naujoks B, Li K, Miettinen K, Yevseyeva I (eds) Evolutionary multi-criterion optimization. Springer Nature Switzerland, Cham, pp 205\u2013217"},{"key":"1465_CR131","doi-asserted-by":"crossref","unstructured":"Tanaka D, Ikami D, Yamasaki T, Aizawa K (2018) Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00582"},{"key":"1465_CR132","doi-asserted-by":"crossref","unstructured":"He X, Tang X, Zheng Z, Zhou Y (2023) Noisy evolutionary optimization with application to grid-based persistent monitoring. IEEE Trans Evol Comput, pp 1\u20131","DOI":"10.1109\/TEVC.2023.3338952"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01465-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01465-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01465-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T17:29:08Z","timestamp":1721237348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01465-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,18]]},"references-count":132,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1465"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01465-5","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,18]]},"assertion":[{"value":"28 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}