Shengxiang Yang    BSc, MSc, PhD, SMIEEE
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Welcome to Shengxiang Yang's Publications Page

Electronic copy of papers is available on request. You may also see my record in Google Scholar, ORCID, Guide2Research, Semantic Scholar, Web Of Science, Scopus, and DBLP.

Books, Proceedings and Journal Special Issues

  1. C. Li, Sanyou Zeng, Ming Yang, and S. Yang. Intelligent Optimization, China University of Geosciences Press, Wuhan, China, ISBN: 978-7-5625-5230-7, 2022.
  2. S. Yang and X. Yao (eds.), Evolutionary Computation for Dynamic Optimization Problems, in the series Studies in Computational Intelligence, vol. 490. Springer, Heidelberg, ISSN: 1860-949X (Print) 1860-9503 (Online), ISBN: 978-3-642-38415-8 (Print) 978-3-642-38416-5 (eBook), May 2013 (Product Flyer, Front Matter, and DOI: 10.1007/978-3-642-38416-5). The book was one of the top 25% most downloaded eBooks in the relevant Springer eBook Collection in 2013.
  3. S. Yang, Y.-S. Ong, and Y. Jin (eds.), Evolutionary Computation in Dynamic and Uncertain Environments, in the series Studies in Computational Intelligence, vol. 51. Springer, Heidelberg, ISSN: 1860-949X (Print) 1860-9503 (Online), ISBN: 978-3-540-49772-1 (Print) 978-3-540-49774-5 (Online), March 2007 (Front Matter and DOI: 10.1007/978-3-540-49774-5).
  4. Jin, Y.; Polikar, R; Yang, S. (eds.), Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4799-4516-0, December 2014. (DOI: 10.1109/CIDUE.2014.7007856).
  5. Jin, Y.; Polikar, R; Yang, S. (eds.), Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4673-5849-1, April 2013 (DOI: 10.1109/CIDUE.2013.6595764).
  6. Jin, Y.; Yang, S.; Polikar, R. (eds.), Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4244-9930-4, April 2011 (DOI: 10.1109/CIDUE.2011.5948495).
  7. Giacobini, M.; Brabazon, A.; Cagnoni, S.; Di Caro, G.A.; Ekart, A.; Esparcia-Alcazar, A.I.; Farooq, M.; Fink, A.; Machado, P.; McCormack, J.; O'Neill, M.; Neri, F.; Preuss, M.; Rothlauf, F.; Tarantino, E.; Yang, S. (eds.), EvoWorkshops 2009: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 5484, Springer, ISBN: 978-3-642-01128-3, April 2009 (DOI: 10.1007/978-3-642-01129-0).
  8. Giacobini, M.; Brabazon, A.; Cagnoni, S.; Di Caro, G.A.; Drechsler, R.; Ekart, A.; Esparcia-Alcazar, A.I.; Farooq, M.; Fink, A.; McCormack, J.; O'Neill, M.; Romero, J.; Rothlauf, F.; Squillero, G.; Uyar, S.; Yang, S. (eds.), EvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4974, Springer, ISBN: 978-3-540-78760-0, May 2008 (DOI: 10.1007/978-3-540-78761-7).
  9. Giacobini, M.; Brabazon, A.; Cagoni, S.; Di Caro, G.A.; Drechsler, R.; Farooq, M.; Fink, A.; Lutton, E.; Machado, P.; Minner, S.; O'Neill, M.; Romero, J.; Rothlauf, F.; Squillero, G.; Takagi, H.; Uyar, A.S.; Yang, S. (eds.), EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4448, Springer, ISBN: 978-3-540-71804-8, June 2007 (DOI: 10.1007/978-3-540-71805-5).
  10. F. Rothlauf et al. (eds.), Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, ACM Press, New York, USA, 2005 (DOI: 10.1145/1102256).
  11. H. Cheng, S. Yang, X. Yao and M. Zhang (guest editors), Computational Intelligence for Cloud Computing, Special Issue of IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 2, No. 1, pp. 1-50, February 2018, IEEE Press, ISSN: 2471-285X (Online).
  12. F. Neri and S. Yang (guest editors), Memetic Computing in the Presence of Uncertainties, Thematic Issue of Memetic Computing, Vol. 2, No. 2, pp. 85-162, June 2010, Springer, ISSN: 1865-9284 (Print) 1865-9292 (Online).
  13. S. Yang, Y.-S. Ong, and Y. Jin (guest editors), Evolutionary Computation in Dynamic and Uncertain Environments, Special Issue of Genetic Programming and Evolvable Machines, Vol. 7, No. 4, pp. 293-404, December 2006, Springer Netherlands, ISSN: 1389-2576 (Print) 1573-7632 (Online).

Refereed Journal Papers (and Source Codes)

  1. Shouyong Jiang, Jinglei Guo, Yong Wang, and Shengxiang Yang. Evolutionary multi/many-objective optimisation via bilevel decomposition. IEEE/CAA Journal of Automatica Sinica, accepted on 25 April, 2024. IEEE Press.
  2. Jiawen Deng, Jihui Zhang, and Shengxiang Yang. A hybrid genetic programming algorithm for the distributed assembly scheduling problems with transportation and sequence-dependent setup times. Engineering Optimization, published online first: 23 April, 2024. Taylor & Francis (DOI: 10.1080/0305215X.2024.2335284).
  3. Jinhao Meng, Lei Cai, Shengxiang Yang, Junxin Li, Feifan Zhou, Jichang Peng, and Zhengxiang Song. An empirical-informed model for the early degradation trajectory prediction of lithium-ion battery. IEEE Transactions on Energy Conversion, published online first: 4 April 2024. IEEE Press (DOI: 10.1109/TEC.2024.3385093).
  4. Wei Song, Shaocong Liu, Xinjie Wang, Shengxiang Yang, and Yaochu Jin. Learning to guide particle search for dynamic multi-objective optimization. IEEE Transactions on Cybernetics, published online first: 23 February, 2024. IEEE Press. (DOI: 10.1109/TCYB.2024.3364375).
  5. Jiawen Deng, Jihui Zhang, and Shengxiang Yang. Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm. Cluster Computing, published online first: 28 January 2024. Springer (DOI: 10.1007/s10586-023-04243-z).
  6. Xuwei Zhang, Shixin Liu, Ziyan Zhao, and Shengxiang Yang. A decomposition-based evolutionary algorithm with clustering and hierarchical estimation for multi-objective fuzzy flexible jobshop scheduling. IEEE Transactions on Evolutionary Computation, published online first: 26 January 2024. IEEE Press (DOI: 10.1109/TEVC.2024.3359120).
  7. Guoyu Chen, Yinan Guo, Min Jiang, Shengxiang Yang, Xiaoxiao Zhao, and Dunwei Gong. A subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm. IEEE Transactions on Emerging Topics in Computational Intelligence, published online first: 12 December, 2023. IEEE Press (DOI: 10.1109/TETCI.2023.3336918).
  8. Yue Xu, Dechang Pi, Shengxiang Yang, and Enrico Zio. Knowledge transfer-based multi-factorial evolutionary algorithm for selective maintenance optimization of multi-state complex systems. IEEE Transactions on Reliability, published online first: 31 October, 2023. IEEE Press (DOI: 10.1109/TR.2023.3324701).
  9. J. Chen, S. Yang, C. Fahy, Z. Wang, Y. Guo, and Y. Chen. Online sparse representation clustering for evolving data streams. IEEE Transactions on Neural Networks and Learning Systems, published online first: 27 October 2023. IEEE Press (DOI: 10.1109/TNNLS.2023.3325556).
  10. Juan Zou, Qi Deng, Yuan Liu, Xinjie Yang, Shengxiang Yang, and Jinhua Zheng. A dynamic-niching-based Pareto domination for multimodal multiobjective optimization. IEEE Transactions on Evolutionary Computation, published online first: 18 September 2023. IEEE Press (DOI: 10.1109/TEVC.2023.3316723).
  11. Guoyu Chen, Yinan Guo, Jing Liang, Yong Wang, Dunwei Gong, and Shengxiang Yang. Evolutionary dynamic constrained multiobjective optimization: Test suite and algorithm. IEEE Transactions on Evolutionary Computation, published online first: 11 September 2023. IEEE Press (DOI: 10.1109/TEVC.2023.3313689).
  12. Q. Chen, J. Ding, Gary G. Yen, S. Yang, and T. Chai. Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments. IEEE Transactions on Evolutionary Computation, published online first: 3 February 2023. IEEE Press (DOI: 10.1109/TEVC.2023.3241762).
  13. Yaru Hu, Juan Zou, Jinhua Zheng, Shouyong Jiang, and Shengxiang Yang. A new framework of change response for dynamic multi-objective optimization. Expert Systems with Applications, 248, Article 123344, August 2024. Elsevier (DOI: 10.1016/j.eswa.2024.123344).
  14. Si Long, Jinhua Zheng, Qi Deng, Yuan Liu, Juan Zou, and Shengxiang Yang. A similarity-detection-based evolutionary algorithm for large-scale multimodal multi-objective optimiza-tion. Swarm and Evolutionary Computation, 87, Article 101548, June 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101548).
  15. Xinfu Pang, Yibao Wang, Shengxiang Yang, Lei Cai, Yang Yu. A bi-objective low-carbon economic scheduling method for cogeneration system considering carbon capture and demand response. Expert Systems with Applications, 243, Article 122875, June 2024. Elsevier (DOI: 10.1016/j.eswa.2023.122875).
  16. Y. Li, Q. Zhao, S. Yang, and Y. Guo. Tailoring evolutionary algorithms to solve the multi-objective location-routing problem for biomass waste collection. IEEE Transactions on Evolutionary Computation, 28(2): 489-500, April 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3265869).
  17. Yinan Guo, Jiayang Pu, Botao Jiao, Yanyan Peng, Dini Wang, and Shengxiang Yang. Online semi-supervised active learning ensemble classification for evolving imbalanced data streams. Applied Soft Computing, 155, Article 111452, April 2024. Elsevier. (DOI: 10.1016/j.asoc.2024.111452).
  18. Lei Cai, Junxin Li, Xianfeng Xu, Haiyan Jin, Jinhao Meng, Bin Wang, Chunling Wu, and Shengxiang Yang. Automatically constructing a health indicator for lithium-ion battery state-of-health estimation via adversarial and compound staked autoencoder. Journal of Energy Storage, 84, Part B, Article 110711, April 2024. Elsevier (DOI: 10.1016/j.est.2024.110711).
  19. J. Chen, S. Yang, X. Peng, D. Peng, and Z. Wang. Augmented sparse representation for incomplete multiview clustering. IEEE Transactions on Neural Networks and Learning Systems, 35(3): 4058-4071, March 2024. IEEE Press (DOI: 10.1109/TNNLS.2022.3201699).
  20. L. Cai, N. Cui, H. Jin, J. Meng, S. Yang, J. Peng, and X. Zhao. A unified deep learning optimization paradigm for Lithium-ion battery state-of-health estimation. IEEE Transactions on Energy Conversion, 39(1): 589-600, March 2024. IEEE Press (DOI: 10.1109/TEC.2023.3294540).
  21. Kangyu Xu, Yizhang Xia, Juan Zou, Zhanglu Hou, Shengxiang Yang, Yaru Hu, and Yuan Liu. A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization. Information Sciences, 661, Article 120193, March 2024. Elsevier (DOI: 10.1016/j.ins.2024.120193).
  22. J. Cheng, Z. Zheng, Y. Guo, J. Pu, S. Yang. Active broad learning with multi-objective evolution for data stream classification. Complex & Intelligent Systems, 10(1): 899-916, February 2024. Springer (DOI: 10.1007/s40747-023-01154-9).
  23. J. Zou, R. Sun, Y. Liu, Y. Hu, S. Yang, J. Zheng, and K. Li. A multi-population evolutionary algorithm using new cooperative mechanism for solving multi-objective problems with multi-constraint. IEEE Transactions on Evolutionary Computation, 28(1): 267-280, February 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3260306).
  24. Y. Hu, J. Zheng, S. Jiang, S. Yang, J. Zou, and R. Wang. A Mahalanobis distance-based approach for dynamic multi-objective optimization with stochastic changes. IEEE Transactions on Evolutionary Computation, 28(1): 238-251, February 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3253850).
  25. Jinze Liu, Jian Feng, Shengxiang Yang, Huaguang Zhang, and Shaoning Liu. Dynamic ε-multilevel hierarchy constraint optimization with adaptive boundary constraint handling technology. Applied Soft Computing, 152, Article 111172, February 2024. Elsevier (DOI: 10.1016/j.asoc.2023.111172).
  26. Yu Sun, Yuqing Chang, Shengxiang Yang, and F. Wang. Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multiobjective optimization. Information Sciences, 653, Article 119794, January 2024. Elsevier (DOI: 10.1016/j.ins.2023.119794).
  27. Yi Xiang, Jinhua Zheng, Yaru Hu, Yuan Liu, Juan Zou, Qi Deng, and Shengxiang Yang. Weak relationship indicator-based evolutionary algorithm for multimodal multi-objective optimization. Information Sciences, 652, Article 119755, January 2024. Elsevier (DOI: 10.1016/j.ins.2023.119755).
  28. J. Wang, C. Li, S. Zeng, and S. Yang. History-guided hill exploration for evolutionary computation. IEEE Transactions on Evolutionary Computation, 27(6): 1962-1975, December 2023. IEEE Press (DOI: 10.1109/TEVC.2023.3250347).
  29. Jian Feng, Shaoning Liu, Shengxiang Yang, Jun Zheng, and Jinze Liu. An adaptive trade-off evolutionary algorithm with composite differential evolution for constrained multi-objective optimization. Swarm and Evolutionary Computation, 83, Article 101386, December 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101386).
  30. Jintong Yang, Juan Zou, Shengxiang Yang, Yaru Hu, Jinhua Zheng, and Yuan Liu. A particle swarm algorithm based on the dual search strategy for dynamic multi-objective optimization. Swarm and Evolutionary Computation, 83, Article 101385, December 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101385).
  31. Y. Xu, Y. Song, D. Pi, Y. Chen, S. Qin, X. Zhang, and S. Yang. A reinforcement learning-based multi-objective optimization in an interval and dynamic environment. Knowledge-Based Systems, 280, Article 111019, November 2023. Elsevier (DOI: 10.1016/j.knosys.2023.111019).
  32. W. Huang, J. Zou, Y. Liu, S. Yang, and J. Zheng. Global and local feasible solution search for solving constrained multi-objective optimization. Information Sciences, 649, Article 119467, November 2023. Elsevier (DOI: 10.1016/j.ins.2023.119467).
  33. R. Sun, J. Zou, Y. Liu, S. Yang, and J. Zheng. A multistage algorithm for solving multiobjective optimization problems with multiconstraints. IEEE Transactions on Evolutionary Computation, 27(5): 1207-1219, October 2023. IEEE Press (DOI: 10.1109/TEVC.2022.3224600 and Source Code in Matlab).
  34. J. Chen, Z. Wang, S. Yang, and H. Mao. Two-stage sparse representation clustering for dynamic data streams. IEEE Transactions on Cybernetics, 53(10): 6408-6420, October 2023. IEEE Press (DOI: 10.1109/TCYB.2022.3204894).
  35. Yinan Guo, Yao Huang, Shirong Ge, Yizhe Zhang, Ersong Jiang, Bin Cheng, and Shengxiang Yang. Low-carbon routing based on improved artificial bee colony algorithm for electric trackless rubber-tyred vehicles. Complex System Modeling and Simulation, 3(3): 169-190, September 2023. Tsinghua University Press (DOI: 10.23919/CSMS.2023.0011).
  36. S. Wang, J. Zheng, Y. Liu, J. Zou, and S. Yang. An extended fuzzy decision variables framework for solving large-scale multiobjective optimization problems. Information Sciences, 643, Article 119221, September 2023. Elsevier (DOI: 10.1016/j.ins.2023.119221).
  37. X. Gong, Z. Rong, J. Wang, K. Zhang, and S. Yang. A hybrid algorithm based on state-adaptive slime mold model and fractional-order ant system for the travelling salesman problem. Complex & Intelligent Systems, 9(4): 3951-3970, August 2023. Springer (DOI: 10.1007/s40747-022-00932-1).
  38. Z. Zhong, Y. Guo, J. Zhang, and S. Yang. Energy-aware integrated scheduling for container terminals with conflict-free AGVs. Journal of Systems Science and Systems Engineering, 32(4): 413-443, August 2023. Springer (DOI: 10.1007/s11518-023-5563-y).
  39. J. Chen, S. Yang, Z. Wang, and H. Mao. Efficient sparse representation for learning in high-dimensional data. IEEE Transactions on Neural Networks and Learning Systems, 34(8): 4208-4222, August 2023. IEEE Press (DOI: 10.1109/TNNLS.2021.3119278).
  40. B. Jiao, Y. Guo, S. Yang, J. Pu, and D. Gong. Reduced-space multistream classification based on multi-objective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 27(4): 764-777, August 2023. IEEE Press (DOI: 10.1109/TEVC.2022.3232466).
  41. J. Zhao, W. Zhang, T. Hu, O. Xu, S. Yang, and Q. Zhang. A hybrid mode membrane computing based algorithm with applications for proton exchange membrane fuel cells. Mathematics, 11(14), Article 3054, July 2023. MDPI (DOI: 10.3390/math11143054).
  42. J. Zheng, B. Zhang, J. Zou, S. Yang, and Y. Hu. A dynamic multi-objective evolutionary algorithm based on niche prediction strategy. Applied Soft Computing, 142, Article 110359, July 2023. Elsevier (DOI: 10.1016/j.asoc.2023.110359).
  43. F. Wang, M. Huang, S. Yang, and X. Wang. Penalty and prediction methods for dynamic constrained multi-objective optimization. Swarm and Evolutionary Computation, 80, Article 101317, July 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101317).
  44. J. Zou, J. Luo, Y. Liu, S. Yang, and J. Zheng. A flexible two-stage constrained multi-objective evolutionary algorithm based on autonomic regulation. Information Sciences, 634: 227-243, July 2023. Elsevier (DOI: 10.1016/j.ins.2023.03.023).
  45. X. Yang, J. Zou, S. Yang, J. Zheng, and Y. Liu. A fuzzy decision variables framework for large-scale multiobjective optimization. IEEE Transactions on Evolutionary Computation, 27(3): 445-459, June 2023. IEEE Press (DOI: 10.1109/TEVC.2021.3118593).
  46. Y. Zou, Y. Liu, J. Zou, S. Yang, and J. Zheng. An evolutionary algorithm based on dynamic sparse grouping for sparse large scale multiobjective optimization. Information Sciences, 631: 449-467, June 2023. Elsevier (DOI: 10.1016/j.ins.2023.02.062).
  47. L. Wang, Q. Zhang, X. He, S. Yang, S. Jiang, and Y. Dong. Biological survival optimization algorithm with its engineering and neural network applications. Soft Computing, 27(10): 6437–6463, May 2023. Springer (DOI: 10.1007/s00500-023-07851-4).
  48. X. Li, X. Li, K. Wang, and S. Yang. A strength Pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts. Information Sciences, 626: 658-693, May 2023. Elsevier (DOI: 10.1016/j.ins.2023.01.073).
  49. C. Fahy, S. Yang, and M. Gongora. Classification in dynamic data streams with a scarcity of labels. IEEE Transactions on Knowledge and Data Engineering, 35(4): 3512-3524, April 2023. IEEE Press (DOI: 10.1109/TKDE.2021.3135755).
  50. S. Calderon-Ramirez, L. Oala, J. Torrents-Barrena, S. Yang, D. Elizondo, A. Moemeni, S. Colreavy-Donnelly, W. Samek, M. A. Molina-Cabello, and E. Lopez-Rubio. Dataset similarity to assess semi-supervised learning under distribution mismatch between the labelled and unlabelled datasets. IEEE Transactions on Artificial Intelligence, 4(2): 282-291, April 2023. IEEE Press (DOI: 10.1109/TAI.2022.3168804).
  51. Y. Hu, J. Zheng, S. Jiang, S. Yang, and J. Zou. Handling dynamic multi-objective optimization environments via layered prediction and subspace-based diversity maintenance. IEEE Transactions on Cybernetics, 53(4): 2572-2585, April 2023. IEEE Press (DOI: 10.1109/TCYB.2021.3128584).
  52. J. Zheng, F. Zhou, J. Zou, S. Yang, and Y. Hu. A dynamic multi-objective optimization based on a hybrid of pivot points prediction and diversity strategies. Swarm and Evolutionary Computation, 78, Article 101284, April 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101284).
  53. J. Zheng, Q. Wu, J. Zou, S. Yang, and Y. Hu. A dynamic multi-objective evolutionary algorithm using adaptive reference vector and linear prediction. Swarm and Evolutionary Computation, 78, Article 101281, April 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101281).
  54. Z. Zheng, S. Yang, Y. Guo, X. Jin, and R. Wang. Meta-heuristics in microgrid management: A survey. Swarm and Evolutionary Computation, 78, Article 101256, April 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101256).
  55. S. Jiang, J. Zou, S. Yang, and X. Yao. Evolutionary dynamic multi-objective optimization: A survey. ACM Computing Survey, 55(4), Article 76, pp. 1-47, April 2023. ACM Press (DOI: 10.1145/3524495).
  56. L. Yan, W. Qi, A. K. Qin, S. Yang, D. Gong, B. Qu, J. Liang. Manifold clustering-based prediction for dynamic multiobjective optimization. Swarm and Evolutionary Computation, 77, Article 101254, March 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101254).
  57. J. Zheng, Z. Du, J. Zou, and S. Yang. A weight vector generation based on normal distribution for preference-based multi-objective optimization. Swarm and Evolutionary Computation, 77, Article 101250, March 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101250).
  58. K. Yang, J. Zheng, J. Zou, F. Yu, and S. Yang. A dual-population evolutionary algorithm based on adaptive constraint strength for constrained multi-objective optimization. Swarm and Evolutionary Computation, 77, Article 101247, March 2023. Elsevier (DOI: 10.1016/j.swevo.2023.101247).
  59. M. Chen, Y. Guo, Y. Jin, S. Yang, D. Gong, and Z. Yu. An environment-driven hybrid evolutionary algorithm for dynamic multi-objective optimization problems. Complex & Intelligent Systems, 9(1): 659–675, February 2023. Springer (DOI: 10.1007/s40747-022-00824-4).
  60. C. Fahy, S. Yang, and M. Gongora. Scarcity of labels in non-stationary data streams: A survey. ACM Computing Surveys, 55(2), Article 40, pp. 1-39, February 2023. ACM Press (DOI: 10.1145/3494832).
  61. Y. Xu, D. Pi, S. Yang, Y. Chen, S. Qin, and E. Zio. An angle-based bi-objective optimization algorithm for redundancy allocation in presence of interval uncertainty. IEEE Transactions on Automation Science and Engineering, 20(1): 271-284, January 2023. IEEE Press (DOI: 10.1109/TASE.2022.3148459).
  62. J. Zhang, J. Zou, S. Yang, and J. Zheng. An evolutionary algorithm based on independently evolving sub-problems for multimodal multi-objective optimization. Information Sciences, 619: 908-929, January 2023. Elsevier (DOI: 10.1016/j.ins.2022.10.096).
  63. S. Calderon-Ramirez, S. Yang, and D. Elizondo. Semi-supervised deep learning for image classification with distribution mismatch: A survey. IEEE Transactions on Artificial Intelligence, 3(6): 1015-1029, December 2022. IEEE Press (DOI: 10.1109/TAI.2022.3196326).
  64. L. Ma, N. Li, Y. Guo, X. Wang, S. Yang, M. Huang, and H. Zhang. Learning to optimize: Reference vector reinforcement learning adaption to constrained many-objective optimization of industrial copper burdening system. IEEE Transactions on Cybernetics, 52(12): 12698-12711, December 2022. IEEE Press (DOI: 10.1109/TCYB.2021.3086501).
  65. Y. Wang, Y. Liu, J. Zou, J. Zheng, and S. Yang. A novel two-phase evolutionary algorithm for solving constrained multiobjective optimization problems. Swarm and Evolutionary Computation, 75, Article 101166, December 2022. Elsevier (DOI: 10.1016/j.swevo.2022.101166).
  66. Z. Zhang, J. Zou, S. Yang, J. Zheng, D. Gong, and T. Pei. A reconstruction method for cross-cut shredded documents based on the extreme learning machine algorithm. Soft Computing, 26: 12851-12862, November 2022. Springer (DOI: 10.1007/s00500-022-07311-5).
  67. J. Chen, S. Yang, H. Mao, and C. Fahy. Multiview subspace clustering using low-rank representation. IEEE Transactions on Cybernetics, 52(11): 12364-12378, November 2022. IEEE Press (DOI: 10.1109/TCYB.2021.3087114).
  68. Y. Guo, J. Feng, B. Jiao, N. Cui, S. Yang, and Z. Yu. A dual evolutionary bagging for class imbalance learning. Expert Systems with Applications, 206, Article 117843, November 2022. Elsevier (DOI: 10.1016/j.eswa.2022.117843).
  69. J. Chen, S. Yang, and Z. Wang. Multi-view representation learning for data stream clustering. Information Sciences, 613: 731-746, October 2022. Elsevier (DOI: 10.1016/j.ins.2022.09.045).
  70. X. Xiang, Y. Tian, R. Chen, X. Zhang, S. Yang, and Y. Jin. A benchmark generator for online dynamic single-objective and multi-objective optimization problems. Information Sciences, 613: 591-608, October 2022. Elsevier (DOI: 10.1016/j.ins.2022.09.049).
  71. M. Fox, S. Yang, and F. Caraffini. A new moving peaks benchmark with attractors for evaluating evolutionary dynamic optimization algorithms. Swarm and Evolutionary Computation, 74, Article 101125, October 2022. Elsevier (DOI: 10.1016/j.swevo.2022.101125).
  72. Y. Wang, X. Li, K.-C. Wong, Y. Chang, and S. Yang. Evolutionary multiobjective clustering algorithms with ensemble for patient stratification. IEEE Transactions on Cybernetics, 52(10): 11027-11040, October 2022. IEEE Press (DOI: 10.1109/TCYB.2021.3069434).
  73. Y. Chen, D. Pi, S. Yang, Y. Xu, J. Chen, and A. W. Mohamed. HINO: An optimization algorithm for energy minimization of UAV-assisted mobile edge computing. IEEE Transactions on Network and Service Management, 19(3): 3264-3275, September 2022. IEEE Press (DOI: 10.1109/TNSM.2022.3176829).
  74. Q. Zhang, X. He, S. Yang, Y. Dong, H. Song, and S. Jiang. Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm. Information Sciences, 610: 868-886, September 2022. Elsevier (DOI: 10.1016/j.ins.2022.08.020).
  75. S. Qi, J. Zou, S. Yang, Y. Jin, J. Zheng, and X. Yang. A self-exploratory competitive swarm optimization algorithm for large-scale multiobjective optimization. Information Sciences, 609: 1601-1620, September 2022. Elsevier (DOI: 10.1016/j.ins.2022.07.110).
  76. Y. Wang, C. Biao, K. C. Wong, X. Li, and S. Yang. Multiobjective deep clustering and its applications in single-cell RNA-seq data. IEEE Transactions on Systems, Man and Cybernetics: Systems, 52(8): 5016-5027, August 2022. IEEE Press (DOI: 10.1109/TSMC.2021.3112049).
  77. B. Xu, D. Gong, Y. Zhang, S. Yang, L. Wang, and Y. Zhang. Cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables. Information Sciences, 607: 278-296, August, 2022. Elsevier (DOI: 10.1016/j.ins.2022.05.123).
  78. Q. Liu, J. Zou, S. Yang, and J. Zheng. A multiobjective evolutionary algorithm based on decision variable classification for many-objective optimization. Swarm and Evolutionary Computation, 73, Article 101108, August 2022. Elsevier (DOI: 10.1016/j.swevo.2022.101108).
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  174. M. Li, S. Yang, and X. Liu. Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228: 45-65, November, 2015. Elsevier (DOI: 10.1016/j.artint.2015.06.007).
  175. M. Mavrovouniotis and S. Yang. Training neural networks with ant colony optimization algorithms for pattern classification. Soft Computing, 19(6): 1511-1522, June, 2015. Springer (DOI: 10.1007/s00500-014-1334-5).
  176. C. Li, T. T. Nguyen, M. Yang, S. Yang, and S. Zeng. Multi-population methods in unconstrained continuous dynamic environments: the challenges. Information Sciences, 296: 95-118, March 2015. Elsevier (DOI: 10.1016/j.ins.2014.10.062).
  177. M. Mavrovouniotis and S. Yang. Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Information Sciences, 294: 456-477, February 2015. Elsevier (DOI: 10.1016/j.ins.2014.10.002 and Source Code in C++).
  178. M. Li, S. Yang, and X. Liu. Diversity comparison of Pareto front approximations in many-objective optimization. IEEE Transactions on Cybernetics, 44(12): 2568-2584, December 2014. IEEE Press (DOI: 10.1109/TCYB.2014.2310651 and Source Code in C).
  179. C. Li, S. Yang, and M. Yang. An adaptive multi-swarm optimizer for dynamic optimization problems. Evolutionary Computation, 22(4): 559-594, Winter 2014. The MIT Press (DOI: 10.1162/EVCO_a_00117 and Source Code in C++ available here).
  180. R. Tinos and S. Yang. Analysis of fitness landscape modifications in evolutionary dynamic optimization. Information Sciences, 282: 214-236, October 2014. Elsevier (DOI: 10.1016/j.ins.2014.05.053 and Source Code in C++ for the implemented DOP benchmark generator).
  181. M. Li, S. Yang, K. Li, and X. Liu. Evolutionary algorithms with segment-based search for multiobjective optimization problems. IEEE Transactions on Cybernetics, 44(8): 1295-1313, August 2014. IEEE Press (DOI: 10.1109/TCYB.2013.2282503).
  182. W. Kong, T. Chai, J. Ding, and S. Yang. Multifurnace optimization in electric smelting plants by load scheduling and control. IEEE Transactions on Automation Science and Engineering, 11(3): 850-862, July 2014. IEEE Press (DOI: 10.1109/TASE.2014.2309348).
  183. M. Li, S. Yang, and X. Liu. Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Transactions on Evolutionary Computation, 18(3): 348-365, June 2014. IEEE Press (DOI: 10.1109/TEVC.2013.2262178 and Source Code in C).
  184. M. Li, S. Yang, J. Zheng, and X. Liu. ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization. Evolutionary Computation, 22(2): 189-230, Summer 2014. The MIT Press (DOI: 10.1162/EVCO_a_00106 and Source Code in C).
  185. S. Yang, M. Li, X. Liu, and J. Zheng. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 17(5): 721-736, October 2013. IEEE Press (DOI: 10.1109/TEVC.2012.2227145 and Source Code in C).
  186. M. Mavrovouniotis and S. Yang. Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Applied Soft Computing, 13(10): 4023-4037, October 2013. Elsevier (DOI: 10.1016/j.asoc.2013.05.022 and Source Code in C++).
  187. S. Yang, Y. Jiang, and T. T. Nguyen. Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 24(4): 451-480, October 2013. Oxford University Press (Invited survey paper, DOI: 10.1093/imaman/DPS021).
  188. Y. Cui, M. Huang, S. Yang, L. H. Lee, and X. Wang. Fourth party logistics routing problem model with fuzzy duration time and cost discount. Knowledge-Based Systems, 50: 14-24, September, 2013. Elsevier (DOI: 10.1016/j.knosys.2013.04.020).
  189. M. Huang, Y. Cui, S. Yang, and X. Wang. Fourth party logistics routing problem with fuzzy duration time. International Journal of Production Economics, 145(1): 107-116, September 2013. Elsevier (DOI: 10.1016/j.ijpe.2013.03.007).
  190. I. Korejo, S. Yang, K. Brohi, and Z.U.A. Khuhro. Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization Problems. International Journal on Soft Computing, Artificial Intelligence and Application, 2(2): 1-19, April 2013. Academy and Industry Research Collaboration Center (AIRCC) (DOI: 10.5121/ijscai.2013.2201).
  191. W. Kong, T. Chai, S. Yang, and J. Ding. A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing. Applied Soft Computing, 13(5): 2960-2969, March 2013. Elsevier (DOI: 10.1016/j.asoc.2012.02.025).
  192. H. Cheng, S. Yang, and J. Cao. Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks. Expert Systems with Applications, 40(4): 1381-1392, March 2013. Elsevier (DOI: 10.1016/j.eswa.2012.08.050).
  193. T. T. Nguyen, S. Yang, and J. Branke. Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6: 1-24, October 2012. Elsevier (Invited survey paper, DOI: 10.1016/j.swevo.2012.05.001).
  194. H. Cheng, S. Yang, and X. Wang. Immigrants enhanced multi-population genetic algorithms for dynamic shortest path routing problems in mobile ad hoc networks. Applied Artificial Intelligence, 26(7): 673-695, August 2012. Taylor & Francis (DOI: 10.1080/08839514.2012.701449).
  195. C. Li and S. Yang. A general framework of multi-population methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation, 16(4): 556-577, August 2012. IEEE Press (DOI: 10.1109/TEVC.2011.2169966, PDF File, and Source Code in C++ with details on EAlib available here).
  196. H. Wang, I.-K. Moon, S. Yang, and D. Wang. A memetic particle swarm optimization algorithm for multimodal optimization problems. Information Sciences, 197: 38-52, August 2012. Elsevier (DOI: 10.1016/j.ins.2012.02.016).
  197. H. Wang, S. Yang, W. H. Ip, and D. Wang. A memetic particle swarm optimization algorithm for dynamic multi-modal optimization problems. International Journal of Systems Science, 43(7): 1268-1283, July 2012. Taylor & Francis (DOI: 10.1080/00207721.2011.605966 and PDF File).
  198. C. Li, S. Yang, and T. T. Nguyen. A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 42(3): 627-646, June 2012. IEEE Press (DOI: 10.1109/TSMCB.2011.2171946, PDF File, and Source Code in C++ with details on EAlib available here).
  199. L. Liu, S. Yang, and D. Wang. Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima. Information Sciences, 182(1): 139-155, January 2012. Elsevier (DOI: 10.1016/j.ins.2010.11.013 and PDF File).
  200. S. N. Jat and S. Yang. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling. Journal of Scheduling, 14(6): 617-637, December 2011. Springer (DOI: 10.1007/s10951-010-0202-0 and PDF File).
  201. R. Tinos and S. Yang. Use of the q-Gaussian mutation in evolutionary algorithms. Soft Computing, 15(8): 1523-1549, August 2011. Springer (DOI: 10.1007/s00500-010-0686-8, PDF File and Source Code in C++).
  202. M. Mavrovouniotis and S. Yang. A memetic ant colony optimization algorithm for the dynamic traveling salesman problem. Soft Computing, 15(7): 1405-1425, July 2011. Springer (DOI: 10.1007/s00500-010-0680-1 and PDF File).
  203. R. Tinos and S. Yang. Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems. International Journal of Hybrid Intelligent Systems, 8(3): 155-168, June 2011. IOS Press (DOI: 10.3233/HIS-2011-0136 and PDF File).
  204. I. Comsa, C. Grosan, and S. Yang. A brief analysis of evolutionary algorithms for the dynamic multiobjective subset sum problem. Studia Univ. Babes-Bolyai, Informatica, LVI(2): 88-94, June 2011. Babes-Bolyai University, Romania (PDF File).
  205. H. Cheng and S. Yang. Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods. Applied Soft Computing, 11(2): 1953-1964, March 2011. Elsevier (DOI: 10.1016/j.asoc.2010.06.011, PDF File, and Source Code in C++).
  206. X. Peng, X. Gao, and S. Yang. Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Computing, 15(2): 311-326, February 2011. Springer (DOI: 10.1007/s00500-010-0547-5, PDF File, and Source Code in Microsoft Visual C++ 6.0).
  207. S. Yang and S. N. Jat. Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 41(1): 93-106, January 2011. IEEE Press (DOI: 10.1109/TSMCC.2010.2049200 and PDF File).
  208. S. Yang and C. Li. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation, 14(6): 959-974, December 2010. IEEE Press (DOI: 10.1109/TEVC.2010.2046667, PDF File, and Source Code in GNU C++).
  209. L. Liu, S. Yang, and D. Wang. Particle swarm optimization with composite particles in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 40(6): 1634-1648, December 2010. IEEE Press (DOI: 10.1109/TSMCB.2010.2043527 and PDF File).
  210. H. Wang, S. Yang, W. H. Ip, and D. Wang. A particle swarm optimization based memetic algorithm for dynamic optimization problems. Natural Computing, 9(3): 703-725, September 2010. Springer (DOI: 10.1007/s11047-009-9176-2 and PDF File).
  211. H. Cheng and S. Yang. Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Engineering Applications of Artificial Intelligence, 23(5): 806-819, August 2010. Elsevier (DOI: 10.1016/j.engappai.2010.01.021, PDF File, C++ Source Code for the General Dynamics Model, and C++ Source Code for the Worst Dynamics Model).
  212. H. Cheng, X. Wang, S. Yang, M. Huang, and J. Cao. QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms. Journal of Network and Computer Applications, 33(4): 512-522, July 2010. Elsevier (DOI: 10.1016/j.jnca.2010.01.001 and PDF File).
  213. S. Yang, D. Wang, T. Chai, and G. Kendall. An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1): 17-38, February 2010. Springer (DOI: 10.1007/s10951-009-0106-z and PDF File).
  214. S. Yang, H. Cheng, and F. Wang. Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 40(1): 52-63, January 2010. IEEE Press (DOI: 10.1109/TSMCC.2009.2023676, PDF File, and Source Code in C++).
  215. H. Wang, S. Yang, W. H. Ip, and D. Wang. Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6): 1348-1361, December 2009. IEEE Press (DOI: 10.1109/TSMCB.2009.2015281, and PDF File).
  216. H. Richter and S. Yang. Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Computing, 13(12): 1163-1173, October 2009. Springer (DOI: 10.1007/s00500-009-0420-6 and PDF File).
  217. H. Wang, D. Wang, and S. Yang. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 13(8-9): 763-780, July 2009. Springer (DOI: 10.1007/s00500-008-0347-3, PDF File, and Source Code in Java).
  218. H. Cheng, J. Cao, X. Wang, S. K. Das, and S. Yang. Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility. Wireless Communications and Mobile Computing, 9(6): 759-771, June 2009. John Wiley & Sons, Ltd (DOI: 10.1002/wcm.627 and PDF File).
  219. H. Cheng, X. Wang, S. Yang, and M. Huang. A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks. Applied Soft Computing, 9(2): 677-684, March 2009. Elsevier (DOI: 10.1016/j.asoc.2008.09.008 and PDF File).
  220. S. Yang and X. Yao. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, October 2008. IEEE Press (DOI: 10.1109/TEVC.2007.913070, PDF File, and Source Code in GNU C++).
  221. S. Yang. Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evolutionary Computation, 16(3): 385-416, Fall 2008. The MIT Press (DOI: 10.1162/evco.2008.16.3.385, PDF File, and Source Code in GNU C++).
  222. R. Tinos and S. Yang. A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines, 8(3): 255-286, September 2007. Springer (DOI: 10.1007/s10710-007-9024-z and PDF File).
  223. S. Yang and R. Tinos. A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, 4(3): 243-254, July 2007. Springer (DOI: 10.1007/s11633-007-0243-9, PDF File, and Source Code in GNU C++).
  224. S. Yang and X. Yao. Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11): 815-834, November 2005. Springer (DOI: 10.1007/s00500-004-0422-3 and PDF File).
  225. S. Yang. Adaptive group mutation for tackling deception in genetic search. WSEAS Transactions on Systems, 3(1): 107-112, January 2004 (PDF File).
  226. S. Yang and D. Wang. A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Computers and Operations Research, 28(10): 955-971, September 2001. Elsevier Science Ltd (DOI: 10.1016/S0305-0548(00)00018-6 and PDF File).
  227. S. Yang and D. Wang. Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling. IEEE Transactions on Neural Networks, 11(2): 474-486, March 2000. IEEE Press (DOI: 10.1109/72.839016 and PDF File).

Refereed Journal Papers (in Chinese)

  1. Jing Sun, Yaoguo Dang, Shengxiang Yang, Junjie Wang, and Shaowen Yang. Grey difference incidence model of panel data and its application. Control and Decision, accepted 18 January, 2024 (URL: http://kzyjc.alljournals.cn/kzyjc/article/abstract/2023-0354).
  2. K. Xu, Y. Liu, M. Li, S. Yang, J. Zou, and J. Zheng. Evolutionary many-objective optimization：A survey. Control Engineering of China, 30(8): 1436-1449, August 2023. (DOI: 10.14107/j.cnki.kzgc.20230186).
  3. J. Zheng, J. Dong, G. Ruan, J. Zou, S. Yang. High-dimensional multi-objective optimization strategy based on decision space oriented search. Ruan Jian Xue Bao/Journal of Software, 30(9): 2686-2704, 2019. (DOI: 10.13328/j.cnki.jos.005842).
  4. X. Zhang and S. Yang. Forecasting the cost of municipal engineering based on PCA and NARX neural network. Control Engineering of China, 24(12): 2485-2490, December 2017. (URL: http://journal13.magtech.org.cn/Jweb_kzgc/EN/1671-7848/home.shtml).
  5. H. Wang, D. Wang, and S. Yang. Evolutionary algorithms in dynamic environments. Control and Decision, 22(2): 127-131+137, February 2007. (URL: http://kzyjc.alljournals.cn/kzyjc/article/abstract/2007-2-2?st=article_issue)
  6. S. Yang and D. Wang. A neural network and heuristics hybrid strategy for job-shop scheduling problem. Journal of Systems Engineering, 14(2): 140-144, June 1999 (PDF File).
  7. S. Yang and D. Wang. Using constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. Information and Control, 28(2): 121-126, April 1999 (PDF File).
  8. S. Yang and D. Wang. Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems. Control and Decision, 13(Suppl.): 402-407, July 1998 (PDF File).
  9. S. Yang and D. Wang. Solving optimization and scheduling problems with neural network methods. Systems Engineering, 15(Suppl.): 66-71, December 1997 (PDF File).

Non-Refereed Journal Papers

  1. H. Cheng, S. Yang, X. Yao and M. Zhang. Guest editorial: Computational intelligence for cloud computing. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1): 1-2, February 2018, IEEE Press (DOI: 10.1109/TETCI.2017.2788548).
  2. F. Neri and S. Yang. Guest editorial: Memetic computing in the presence of uncertainties. Memetic Computing, 2(2): 85-86, June 2010. Springer (DOI: 10.1007/s12293-010-0033-8 and PDF File).
  3. S. Yang, Y.-S. Ong, and Y. Jin. Editorial to special issue on evolutionary computation in dynamic and uncertain environments. Genetic Programming and Evolvable Machines, 7(4): 293-294, December 2006. Springer (DOI: 10.1007/s10710-006-9016-4 and PDF File).

Invited / Contributed Book Chapters

  1. C. Li, S. Zeng, and S. Yang. Dynamic multi-objective optimization for multi-objective vehicle routing problem with real-time traffic conditions. In: M. Wu, W. Pedrycz, and L. Chen (Eds), Developments in Advanced Control and Intelligent Automation for Complex Systems. Studies in Systems, Decision and Control, vol. 329, Chapter 11, pp. 289-306, March 2021. Springer, Cham (DOI: 10.1007/978-3-030-62147-6_11).
  2. X. Wu, S. Zeng, C. Li, and S. Yang. A novel multi-objective evolutionary algorithm based on space partitioning. In: K. Li, W. Li, H. Wang, Y. Liu (Eds.) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205, pp. 127-142, May 2020. Springer, Singapore. (DOI: 10.1007/978-981-15-5577-0_10).
  3. M. Mavrovouniotis and S. Yang. Ant colony optimization for dynamic combinatorial optimization problems. In: Y. Tan (Ed.), Swarm Intelligence Volume 1: Principles, Current Algorithms and Methods, Chapter 5, pp. 121-142, 2018. Print: 978-1-78561-627-3, eBook: 9781785616280, The Institution of Engineering and Technology (IET) (DOI: 10.1049/PBCE119F_ch5).
  4. C. Fahy and S. Yang. Dynamic stream clustering using ants. In P. Angelov, A. Gegov, C. Jayne, and Q. Shen (Eds.), Advances in Computational Intelligence Systems, Volume 513 of the series Advances in Intelligent Systems and Computing, Chapter 32, pp. 495-508, 2016. Springer (DOI: 10.1007/978-3-319-46562-3_32).
  5. M. Mavrovouniotis and S. Yang. Dynamic vehicle routing: A memetic ant colony optimization approach. In A. S. Uyar, E. Ozcan, and N. Urquhart (Eds.), Automated Scheduling and Planning, Volume 505 of the series Studies in Computational Intelligence, Chapter 11, pp. 283-301, Springer-Verlag, December 2013 (DOI: 10.1007/978-3-642-39304-4_11).
  6. S. Yang, T. T. Nguyen, and C. Li. Evolutionary dynamic optimization: test and evaluation environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 1, pp. 3-37, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_1).
  7. T. T. Nguyen, S. Yang, J. Branke, and X. Yao. Evolutionary dynamic optimization: methodologies. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 2, pp. 39-64, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_2).
  8. C. Li and S. Yang. A comparative study on particle swarm optimization in dynamic environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 5, pp. 109-136, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_5).
  9. H. Wang and S. Yang. Memetic algorithms for dynamic optimization problems. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 6, pp. 137-170, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_6).
  10. R. Tinos and S. Yang. Analyzing evolutionary algorithms for dynamic optimization problems based on the dynamical systems approach. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 10, pp. 241-267, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_10).
  11. I. Comsa, C. Grosan, and S. Yang. Dynamics in the multi-objective subset sum: analysing the behaviour of population based algorithms. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 12, pp. 299-313, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_12).
  12. M. Mavrovouniotis and S. Yang. Ant colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problem. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 13, pp. 317-341, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_13).
  13. H. Cheng and S. Yang. Genetic algorithms for dynamic routing problems in mobile ad hoc networks. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 14, pp. 343-375, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_14).
  14. X. Peng, S. Yang, D. Xu, and X. Gao. Evolutionary algorithms for the multiple unmanned aerial combat vehicles anti-ground attack problem in dynamic environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 16, pp. 403-431, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_16).
  15. H. Richter and S. Yang. Dynamic optimization using analytic and evolutionary approaches: A comparative review. In I. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, Chapter 1, pp. 1-28, Springer-Verlag Berlin Heidelberg, 2013 (DOI: 10.1007/978-3-642-30504-7_1).
  16. Y. Yan, S. Yang, D. Wang, and D. Wang. Agent based evolutionary dynamic optimization. In R. Sarker and T. Ray (eds.), Agent Based Evolutionary Search, Chapter 5, pp. 97-116, Springer-Verlag Berlin Heidelberg, 2010 (DOI: 10.1007/978-3-642-13425-8_5 and PDF File).
  17. H. Cheng, X. Wang, M. Huang, and S. Yang. A review of personal communications services. In K. Y. Chen and H. K. Lee (Eds.), Mobile Computing Research and Applications, Chapter 8, pp. 149-165, Nova Science Publishers, 3rd Quarter, 2009 (ISBN: 978-1-60741-101-7) (PDF File).
  18. S. Yang. Explicit memory schemes for evolutionary algorithms in dynamic environments. In S. Yang, Y.-S. Ong, and Y. Jin (Eds.), Evolutionary Computation in Dynamic and Uncertain Environments, Volume 51 of the series Studies in Computational Intelligence, Chapter 1, pp. 3-28, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_1 and PDF File).
  19. R. Tinos and S. Yang. Genetic algorithms with self-organizing behaviour in dynamic environments. In S. Yang, Y.-S. Ong, and Y. Jin (Eds.), Evolutionary Computation in Dynamic and Uncertain Environments, Volume 51 of the series Studies in Computational Intelligence, Chapter 5, pp. 105-127, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_5 and PDF File).
  20. S. Yang. Adaptive mutation using statistics mechanism for genetic algorithms. In F. Coenen, A. Preece and A. Macintosh (Eds.), Research and Development in Intelligent Systems XX, pp. 19-32, March 2004. London: Springer-Verlag (DOI: 10.1007/978-0-85729-412-8_2 and PDF File).
  21. S. Yang. PDGA: the primal-dual genetic algorithm. In A. Abraham, M. Koppen and K. Franke (Eds.), Design and Application of Hybrid Intelligent Systems, pp. 214-223, 2003. IOS Press (DOI: 10.13140/RG.2.1.3134.3445 and PDF File).
  22. S. Yang. Genetic algorithms based on primal-dual chromosomes for royal road functions. In A. Grmela and N. E. Mastorakis (Eds.), Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 174-179, 2002. WSEAS Press (PDF File).

Refereed Conference Papers (and Source Codes)

  1. Stephen S. Aremu, Aboozar Taherkhani, Chang Liu, and Shengxiang Yang. 3D object reconstruction with deep learning. Proceedings of the 13th IFIP International Conference on Intelligent Information Processing, pp. xxx-xxx, 2024.
  2. Saneet Fulsunder, Saidu Umar, Aboozar Taherkhani, Chang Liu, and Shengxiang Yang. Hand gesture recognition using a multi-modal deep neural network. Proceedings of the 13th IFIP International Conference on Intelligent Information Processing, pp. xxx-xxx, 2024.
  3. Valentine Oleka, Seyyed Mohsen Zahedi, Aboozar Taherkhani, Reza Baserinia, Abolfazl Zahedi, and Shengxiang Yang. Graph convolutional networks for predicting mechanical characteristics of 3D lattice structures. Proceedings of the 13th IFIP International Conference on Intelligent Information Processing, pp. xxx-xxx, 2024.
  4. Anju Yang, Yuan Liu, Juan Zou, and Shengxiang Yang. Decomposed multi-objective method based on Q-learning for solving multi-objective combinatorial optimization. Proceedings of the 18th International Conference on Bio-inspired Computing: Theories and Applications, pp. xxx-xxx, 2023. Springer.
  5. C. Fahy and S. Yang. An evolving population approach to data-stream classification with extreme verification latency. Proceedings of the 2023 IEEE Symposium Series on Computational Intelligence, pp. 1843-1848, 2023. IEEE Press (DOI: 10.1109/SSCI52147.2023.10371923).
  6. J. Qiu, C. Li, and S. Yang. A reinforcement learning based dynamic multi-objective constrained evolutionary algorithm for open-pit mine truck scheduling. Proceedings of the 2023 China Automation Congress, pp. xxx-xxx, 2023. IEEE Press.
  7. M. Ao, C. Li, and S. Yang. Prediction method of truck travel time in open pit mines based on LSTM model. Proceedings of the 42nd Chinese Control Conference, pp. 8651-8656, 2023. IEEE Press (DOI: 10.23919/CCC58697.2023.10240705).
  8. Y. Ti, C. Li, and S. Yang. A test suite and an optimizer for dietary nutrition optimization problem: From constrained many-objective perspective. Proceedings of the 2023 IEEE Congress on Evolutionary Computation, pp. 1-8, 2023. IEEE Press (DOI: 10.1109/CEC53210.2023.10254170).
  9. Q. Tan, C. Li, S. Zeng, and S. Yang. A subspace-based non-dominated subset selection method. Proceedings of the 2023 IEEE Congress on Evolutionary Computation, pp. 1-8, 2023. IEEE Press (DOI: 10.1109/CEC53210.2023.10254058).
  10. Y. Diao, C. Li, S. Zeng, and S. Yang. Nearest better network for visualization of the fitness landscape. Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion), pp. 815-818, 2023. ACM Press (DOI: 10.1145/3583133.3590654).
  11. S. Wang, J. Zheng, J. Zou, Y. Liu, S. Yang, and Y. Zou. A fuzzy decision variables framework based on directed sampling for large-scale multiobjective optimization. Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (GECCO '23 Companion), pp. 419-422, 2023. ACM Press (DOI: 10.1145/3583133.3590590).
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  106. L. Liu, D. Wang, and S. Yang. An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems. EvoWorkshops 2009: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 5484, pp. 725-734, 2009. Springer (DOI: 10.1007/978-3-642-01129-0_82 and PDF File).
  107. C. Li and S. Yang. An island based hybrid evolutionary algorithm for optimization. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 180-189, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_19 and PDF File).
  108. H. Cheng and S. Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using simulated annealing. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 370-380, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_38 and PDF File).
  109. C. Li and S. Yang. A generalized approach to construct benchmark problems for dynamic optimization. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 391-400, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_40 and PDF File).
  110. S. N. Jat and S. Yang. A memetic algorithm for the university course timetabling problem. Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence, vol. 1, pp. 427-433, 2008. IEEE Press (DOI: 10.1109/ICTAI.2008.126 and PDF File).
  111. H. Cheng, X. Wang, M. Huang, and S. Yang. A review of personal communications services. Proceedings of the 9th International Conference for Young Computer Scientists, pp. 616-621, 2008. IEEE Press (DOI: 10.1109/ICYCS.2008.191).
  112. H. Richter and S. Yang. Learning in abstract memory schemes for dynamic optimization. Proceedings of the 4th International Conference on Natural Computation, vol. 1, pp. 86-91, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.110).
  113. C. Li and S. Yang. Fast multi-swarm optimization for dynamic optimization problems. Proceedings of the 4th International Conference on Natural Computation, vol. 7, pp. 624-628, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.313 and PDF File).
  114. C. Ji, Y. Zhang, M. Tong, and S. Yang. Particle filter with swarm move for optimization. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 5199, pp. 909-918, 2008. Springer (DOI: 10.1007/978-3-540-87700-4_90 and PDF File).
  115. H. Cheng and S. Yang. A genetic-inspired joint multicast routing and channel assignment algorithm in wireless mesh networks. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 159-164, 2008 (PDF File).
  116. C. Li, S. Yang, and I. Korejo. An adaptive mutation operator for particle swarm optimization. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 165-170, 2008 (PDF File).
  117. R. Tinos and S. Yang. Evolutionary programming with q-Gaussian mutation for dynamic pptimization problems. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1823-1830, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631036 and PDF File).
  118. Y. Yan, H. Wang, D. Wang, S. Yang, and D. Z. Wang. A multi-agent based evolutionary algorithm in non-stationary environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 2967-2974, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631198 and PDF File).
  119. S. Yang and R. Tinos. Hyper-selection in dynamic environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 3185-3192, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631229 and PDF File).
  120. H. Richter and S. Yang. Memory based on abstraction for dynamic fitness functions. In EvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4974, pp. 597-606, 2008. Springer (DOI: 10.1007/978-3-540-78761-7_65 and PDF File).
  121. L. Liu, D. Wang, and S. Yang. Compound particle swarm optimization in dynamic environments. In EvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4974, pp. 616-625, 2008. Springer (DOI: 10.1007/978-3-540-78761-7_67 and PDF File).
  122. R. Tinos and S. Yang. Self-adaptation of mutation distribution in evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 79-86, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424457 and PDF File).
  123. R. Tinos and S. Yang. Continuous dynamic problem generators for evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 236-243, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424477 and PDF File).
  124. S. Yang. Learning the dominance in diploid genetic algorithms for changing optimization problems. Proceedings of the 2nd International Symposium on Intelligence Computation and Applications, pp. 157-162, 2007. China University of GeoSciences Press (PDF File).
  125. S. Yang. Genetic algorithms with elitism-based immigrants for changing optimization problems. EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4448, pp. 627-636, 2007. Springer (DOI: 10.1007/978-3-540-71805-5_69 and PDF File).
  126. H. Wang, D. Wang, and S. Yang. Triggered memory-based swarm optimization in dynamic environments. EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4448, pp. 637-646, 2007. Springer (DOI: 10.1007/978-3-540-71805-5_70 and PDF File).
  127. S. Yang. On the design of diploid genetic algorithms for problem optimization in dynamic environments. Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 1362-1369, 2006. IEEE Press (DOI: 10.1109/CEC.2006.1688467 and PDF File).
  128. S. Yang. Job-shop scheduling with an adaptive neural network and local search hybrid approach. Proceedings of the 2006 IEEE Int. Joint Conf. on Neural Networks, pp. 2720-2727, 2006. IEEE Press (DOI: 10.1109/IJCNN.2006.247176 and PDF File).
  129. S. Yang. A comparative study of immune system based genetic algorithms in dynamic environments. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1377-1384, 2006. ACM Press (DOI: 10.1145/1143997.1144209 and PDF File).
  130. S. Yang. Dominance learning in diploid genetic algorithms for dynamic optimization problems. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1435-1436, 2006. ACM Press (DOI: 10.1145/1143997.1144232 and PDF File).
  131. S. Yang. Associative memory scheme for genetic algorithms in dynamic environments. EvoWorkshops 2006: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 3907, pp. 788-799, 2006. Springer (DOI: 10.1007/11732242_76 and PDF File).
  132. S. Yang and S. Uyar. Adaptive mutation with fitness and allele distribution correlation for genetic algorithms. Proceedings of the 21st ACM Symposium on Applied Computing (SAC'06), pp. 940-944, 2006. ACM Press (DOI: 10.1145/1141277.1141499 and PDF File).
  133. S. Yang. An improved adaptive neural network for job-shop scheduling. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernatics, Vol. 2, pp. 1200-1205, 2005. IEEE Press (DOI: 10.1109/ICSMC.2005.1571309 and PDF File).
  134. S. Yang. Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2560-2567, 2005. IEEE Press (DOI: 10.1109/CEC.2005.1555015 and PDF File).
  135. R. Tinos and S. Yang. Genetic algorithms with self-organized criticality for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2816-2823, 2005. IEEE Press (DOI: 10.1109/CEC.2005.1555048 and PDF File).
  136. S. Yang. Population-based incremental learning with memory scheme for changing environments. Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 1, pp. 711-718, 2005. ACM Press (DOI: 10.1145/1068009.1068128 and PDF File).
  137. S. Yang. Memory-based immigrants for genetic algorithms in dynamic environments. Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005. ACM Press (DOI: 10.1145/1068009.1068196 and PDF File). This paper was nominated to the 2005 Genetic and Evolutionary Computation Conference (GECCO-2005) Best Paper Award.
  138. S. Yang. Constructing dynamic test environments for genetic algorithms based on problem difficulty. Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Vol. 2, pp. 1262-1269, 2004. IEEE Press (DOI: 10.1109/CEC.2004.1331042 and PDF File).
  139. S. Yang. Non-stationary problem optimization using the primal-dual genetic algorithm. In R. Sarker, R. Reynolds, H. Abbass, K.-C. Tan, R. McKay, D. Essam and T. Gedeon (editors), Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2246-2253, 2003. IEEE Press (DOI: 10.1109/CEC.2003.1299951 and PDF File).
  140. S. Yang and X. Yao. Dual population-based incremental learning for problem optimization in dynamic environments. In M. Gen et. al. (editors), Proceedings of the 7th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 49-56, 2003 (PDF File).
  141. S. Yang. Statistics-based adaptive non-uniform mutation for genetic algorithms. In E. Cantu-Paz, J.A. Foster, K. Deb, L. D. Davis, R. Roy, U.-M. O'Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta. M. A. Potter, A. C. Schultz, K. A. Dowsland, N. Jonoska, and J. Miller (editors), Proceedings of the Genetic and Evolutionary Computation Conference - GECCO 2003, Lecture Notes in Computer Science, vol. 2724, pp. 1618-1619, 2003. Springer (DOI: 10.1007/3-540-45110-2_53).
  142. S. Yang. Primal-dual genetic algorithms for royal road functions. In E. F. Camacho, L. Basanez, J. A. de la Puente (editors), Proceedings of the 15th IFAC World Congress, Vol. I: Fuzzy, Neural and Genetic Systems, pp. 373-378, Barcelona, Spain, 21-26 July 2002. Elsevier Science Ltd (DOI: 10.3182/20020721-6-ES-1901.00715).
  143. S. Yang. Statistics-based adaptive non-uniform crossover for genetic algorithms, In J. A. Bullinaria (editor), Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI'02), pp. 201-208, 2002 (PDF File).
  144. S. Yang. Adaptive non-uniform crossover based on statistics for genetic algorithms. In W. B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (editors), Proceedings of the 2002 Genetic and Evolutionary Computation Conference, pp. 650-657, 2002. San Francisco, CA: Morgan Kaufmann Publishers (PDF File).
  145. S. Yang. Adaptive non-uniform mutation based on statistics for genetic algorithms. In Erick Cantu-Paz (editor), Late-Breaking Papers at the 2002 Genetic and Evolutionary Computation Conference, pp. 490-495, 2002. Menlo Park, CA: AAAI Press (PDF File).
  146. S. Yang. Adaptive crossover in genetic algorithms using statistics mechanism. In R. Standish, M. Bedau and H. Abbass (editors), Proceedings of the 8th Int. Conf. on Artificial Life (ALife VIII), pp. 182-185, 2002. MIT Press (PDF File).
  147. T. Radzik and S. Yang. Experimantal evaluation of algorithmic solutions for generalized network flow models. Presented in the 17th International Symposium on Mathematical Programming (ISMP'00), August 2000.
  148. S. Yang and D. Wang. Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. In H. F. Chen, X. R. Cao, G. Picci and K. J. Hunt (editors), Proceedings of the 14th IFAC World Congress, Vol. J: Discrete Event Systems, Stochastic Systems, Fuzzy and Neural Systems I, pp. 175-180, 1999. Elsevier Science Ltd (PDF File).
  149. K. Zhao, S. Yang and D. Wang. Genetic algorithm and neural network hybrid approach for job-shop scheduling. In M. H. Hamza (editor), Proceedings of the IASTED Int. Conf. on Applied Modelling and Simulation (AMS'98), pp. 110-114, 1998. Calgary, Alberta, Canada: ACTA Press (PDF File).

Non-Refereed Conference Publications

  1. S. Yang. Evolutionary computation for dynamic optimization problems. Proceedings of the Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, pp. 629-649, 2015. ACM Press (DOI: 10.1145/2739482.2756589 and PDF File).
  2. S. Yang. Evolutionary computation for dynamic optimization problems. Proceeding of the 15th Annual Conference on Genetic and Evolutionary Computation Companion, pp. 667-682, 2013. ACM Press (DOI: 10.1145/2464576.2480805 and PDF File).
  3. S. Yang and J. Branke. Evolutionary algorithms for dynamic optimization problems: workshop preface. Proceedings of the 2005 Workshops on Genetic and Evolutionary Computation, pp. 23-24, 2005. ACM Press (DOI: 10.1145/1102256.1102261 and PDF File).

Other Workshop Publications

  1. S. Yang and J. Branke (editors), Proceedings of the 4th Workshop on Evolutionary Algorithms for Dynamic Optimization Problem, 2005.

PhD Thesis

  1. S. Yang. Constraint Satisfaction Adaptive Neural Network and its Applications for Job-Shop Scheduling Problems. PhD Thesis, Northeastern University, P. R. China, March 1999.

Technical Reports

  1. S. Jiang, S. Yang, X. Yao and K. C. Tan. Benchmark Functions for the CEC'2018 Competition on Dynamic Multiobjective Optimization. Technical Report, Newcastle University, U.K., January 2018 (PDF File).
  2. R. Cheng, M. Li, Y. Tian, X. Xiang, X. Zhang, S. Yang, Y. Jin and X. Yao. Benchmark Functions for the CEC'2018 Competition on Many-Objective Optimization. Technical Report, CERCIA Group, University of Birmingham, U.K., January 2018 (PDF File).
  3. R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin and X. Yao. Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization. Technical Report No. CSR-17-01, School of Computer Science, University of Birmingham, U.K., January 2017 (PDF File).
  4. C. Li, M. Mavrovouniotis, S. Yang, and X. Yao. Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG). Technical Report 2013, School of Computer Science and Informatics, De Montfort University, U.K., October 2013 (DOI: 10.13140/RG.2.1.1201.0328 and PDF File).
  5. M. Mavrovouniotis, C. Li, S. Yang, and X. Yao. Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Travelling Salesman Problem Benchmark Generator. Technical Report 2013, School of Computer Science and Informatics, De Montfort University, U.K., October 2013 (DOI: 10.13140/RG.2.1.3822.4729 and PDF File).
  6. C. Li, S. Yang, and D. A. Pelta. Benchmark Generator for the IEEE WCCI-2012 Competition on Evolutionary Computation for Dynamic Optimization Problems. Technical Report 2011, Department of Information Systems and Computing, Brunel University, U.K., October 2011 (DOI: 10.13140/RG.2.1.3298.1842 and PDF File).
  7. C. Li, S. Yang, T. T. Nguyen, E. L. Yu, X. Yao, Y. Jin, H.-G. Beyer, and P. N. Suganthan. Benchmark generator for CEC 2009 competition on dynamic optimization. Technical Report 2008, Department of Computer Science, University of Leicester, U.K., October 2008 (DOI: 10.13140/RG.2.1.3445.6401 and PDF File).
  8. S. Yang. A new genetic algorithm based on primal-dual chromosomes for royal road functions. Technical Report No. 2001/45, Department of Computer Science, University of Leicester, U.K., 2001 (DOI: 10.13140/RG.2.1.4871.0485 and PDF File).
  9. T. Radzik and S. Yang. Experimental evaluation of algorithmic solutions for the maximum generalised network flow problem. Technical Report No. 2001/54, Department of Computer Science, University of Leicester, U.K., 2001. It is also available as Technical Report No. TR-01-09, Department of Computer Science, King's College London, U.K., 2001 (DOI: 10.13140/RG.2.1.3822.4729 and PDF File).
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