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Zhang, \u201cMatching large-scale biomedical ontologies with central concept based partitioning algorithm and adaptive compact evolutionary algorithm,\u201d Appl. Soft Comput., vol.106, pp.1-11, July 2021. 10.1016\/j.asoc.2021.107343","DOI":"10.1016\/j.asoc.2021.107343"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] G. Acampora, V. Loia, and A. Vitiello, \u201cEnhancing ontology alignment through a memetic aggregation of similarity measures,\u201d Inf. Sci., vol.250, pp.1-20, Nov. 2013. 10.1016\/j.ins.2013.06.052","DOI":"10.1016\/j.ins.2013.06.052"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] X. Xue, C. Jiang, J. Zhang, H. Zhu, and C. Yang, \u201cMatching sensor ontologies through siamese neural networks without using reference alignment,\u201d PeerJ Comput. Sci., vol.7, no.4, pp.1-22, 2021. 10.7717\/peerj-cs.602","DOI":"10.7717\/peerj-cs.602"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] A. Charpentier, R. \u00c9lie, and C. Remlinger, \u201cReinforcement learning in economics and finance,\u201d Comput. Econ., pp.1-38, April 2021. 10.1007\/s10614-021-10119-4","DOI":"10.1007\/s10614-021-10119-4"},{"key":"8","unstructured":"[8] J. Kreutzer, S. Riezler, and C. Lawrence, \u201cLearning from human feedback: Challenges for real-world reinforcement learning in NLP,\u201d Proc. 5th Workshop on Structured Prediction for NLP, vol.2021, pp.1-7, 2020."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, \u201cHuman-level control through deep reinforcement learning,\u201d Nature, vol.518, no.7540, pp.529-33, Feb. 2015. 10.1038\/nature14236","DOI":"10.1038\/nature14236"},{"key":"10","unstructured":"[10] T.P. Lillicrap, J.J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, \u201cContinuous control with deep reinforcement learning,\u201d Computer Science, 2015. 10.48550\/arXiv.1509.02971"},{"key":"11","unstructured":"[11] I. Erev and A.E. Roth, \u201cPredicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria,\u201d American economic review, vol.88, no.4, pp.848-881, Sept. 1998."},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] X. Xue, P.W. Tsai, and Y. Zhuang, \u201cMatching biomedical ontologies through adaptive multi-modal multi-objective evolutionary algorithm,\u201d Biology, vol.10, no.12, pp.1-16, Dec. 2021. 10.3390\/biology10121287","DOI":"10.3390\/biology10121287"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] X. Xue, J. Lu, and J. 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Wang, \u201cOptimizing ontology alignments through a Memetic Algorithm using both Matchfmeasure and Unanimous Improvement Ratio,\u201d Artificial Intelligence, vol.223, pp.65-81, June 2015. 10.1016\/j.artint.2015.03.001","DOI":"10.1016\/j.artint.2015.03.001"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] X. Xue and Q. Huang, \u201cGenerative adversarial learning for optimizing ontology alignment,\u201d Expert Syst., pp.1-12, Jan. 2022. 10.1111\/exsy.12936","DOI":"10.1111\/exsy.12936"},{"key":"18","unstructured":"[18] V.I. Levenshtein, \u201cBinary codes capable of correcting deletions, insertions, and reversals,\u201d Soviet physics doklady, pp.707-710, Soviet Union, 1966."},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] G.A. Miller, \u201cWordNet: A lexical database for English,\u201d Commun. ACM, vol.38, no.11, pp.39-41, Nov. 1995. 10.1145\/219717.219748","DOI":"10.1145\/219717.219748"},{"key":"20","unstructured":"[20] S. Melnik, H. Garcia-Molina, and E. Rahm, \u201cSimilarity flooding: A versatile graph matching algorithm and its application to schema matching,\u201d 18th International Conference on Data Engineering, Shanghai, China, pp.117-182, April 2002. 10.1109\/ICDE.2002.994702"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] J. Peters and S. Schaal, \u201cReinforcement learning of motor skills with policy gradients,\u201d Neural Netw, vol.21, no.4, pp.682-697, May 2008. 10.1016\/j.neunet.2008.02.003","DOI":"10.1016\/j.neunet.2008.02.003"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] L.P. Kaelbling, M.L. Littman, and A.W. Moore, \u201cReinforcement learning: A survey,\u201d J. Artif. Intell. Res, vol.4, pp.237-285, May 1996. 10.1613\/jair.301","DOI":"10.1613\/jair.301"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] H.V. Hasselt, A. Guez, and D. Silver, \u201cDeep reinforcement learning with Double Q-learning,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.30, no.1, 2015. 10.1609\/aaai.v30i1.10295","DOI":"10.1609\/aaai.v30i1.10295"},{"key":"24","unstructured":"[24] L. Zhu and T. Matsubara, \u201cEnsuring monotonic policy improvement in entropy-regularized value-based reinforcement learning,\u201d arXiv preprint, arXiv:2008.10806, 2020."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] D. Takeyama, M. Kanoh, T. Matsui, and T. Nakamura, \u201cAcquisition by robots of danger-avoidance behaviors using probability-based reinforcement learning,\u201d 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2015. 10.1109\/FUZZ-IEEE.2015.7337999","DOI":"10.1109\/FUZZ-IEEE.2015.7337999"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] X. Xue and J. Chen, \u201cMatching biomedical ontologies through compact differential evolution algorithm with compact adaption schemes on control parameters,\u201d Neurocomputing, vol.458, pp.526-534, Oct. 2021. 10.1016\/j.neucom.2020.03.122","DOI":"10.1016\/j.neucom.2020.03.122"},{"key":"27","unstructured":"[27] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, \u201cPlaying atari with deep reinforcement learning,\u201d Comput. Sci., Jan. 2013. 10.48550\/arXiv.1312.5602"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] J. Koutn\u00edk, J. Schmidhuber, and F. Gomez, \u201cEvolving deep unsupervised convolutional networks for vision-based reinforcement learning,\u201d Proc. 2014 Annual Conference on Genetic and Evolutionary Computation, pp.541-548, July 2014. 10.1145\/2576768.2598358","DOI":"10.1145\/2576768.2598358"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] W. Zeng, X. Zhao, J. Tang, X. Lin, and P. Groth, \u201cReinforcement learning-based collective entity alignment with adaptive features,\u201d ACM Trans. Inf. Syst. (TOIS), vol.39, no.3, pp.1-31, May 2021. 10.1145\/3446428","DOI":"10.1145\/3446428"},{"key":"30","unstructured":"[30] H. Li, N. Kumar, R. Chen, and P. Georgiou, \u201cDeep reinforcement learning,\u201d ICASSP 2018-2018 IEEE International Conference on Acoust., Speech, Signal Process. (ICASSP), 2018."},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] C.M. Theobald, \u201cGeneralizations of mean square error applied to ridge regression,\u201d J. R. Stat. 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