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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, ScholarGPS, ORCID, Guide2Research, AD Scientific Index, Semantic Scholar, Web of Science, Scopus, SciPofiles, and DBLP.

Books, Proceedings and Journal Special Issues

  1. Changhe Li, Shoufei Han, Sanyou Zeng, and Shengxiang Yang. Intelligent Optimization: Principles, Algorithms and Applications, Springer Singapore, ISBN: 978-981-97-3285-2, ISBN 978-981-97-3286-9 (eBook), July 2024. (Front Matter and DOI: 10.1007/978-981-97-3286-9).
  2. Shengxiang Yang and Xin 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. Shengxiang Yang, Yew-Soon Ong, and Yaochu 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. Zhongzhi Shi, Jim Torresen, and Shengxiang Yang (eds.), Intelligent Information Processing XII, Proceedings of 13th IFIP International Conference on Intelligent Information Processing (IIP 2024), Shenzhen, China, May 3–6, 2024, Part I. IFIP Advances in Information and Communication Technology, vol. 703, ISBN: 978-3-031-57918-9. Springer (DOI: 10.1007/978-3-031-57808-3).
  5. Zhongzhi Shi, Jim Torresen, and Shengxiang Yang (eds.), Intelligent Information Processing XII, Proceedings of 13th IFIP International Conference on Intelligent Information Processing (IIP 2024), Shenzhen, China, May 3–6, 2024, Part II. IFIP Advances in Information and Communication Technology, vol. 704, ISBN: 978-3-031-57918-9. Springer (DOI: 10.1007/978-3-031-57919-6).
  6. Yaochu Jin, Robi Polikar, and Shengxiang Yang (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).
  7. Yaochu Jin, Robi Polikar, and Shengxiang Yang (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).
  8. Yaochu Jin, Shengxiang Yang, and Robi Polikar (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).
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. Danial Yazdani, Wenjian Luo, and Shengxiang Yang (guest editors), Evolutionary Dynamic Optimization, Special Issue of IEEE Transactions on Evolutionary Computation, Vol. 29, No. 5, October 2025, IEEE Press, ISSN: 1089-778X (Online).
  14. Hui Cheng, Shengxiang Yang, Xin Yao and Mengjie 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).
  15. Ferrante Neri and Shengxiang 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).
  16. Shengxiang Yang, Yew-Soon Ong, and Yaochu 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. Yaqi Ti, Changhe Li, Xinye Cai, and Shengxiang Yang. A fitness spatially informed evolutionary algorithm for deceptive multi-modal multi-objective optimization. IEEE Transactions on Evolutionary Computation, accepted on 27 September 2025. IEEE Press.
  2. Chi Zhang, Tai Xiong, Miao Rong, Dunwei Gong, and Shengxiang Yang. AdaptiveStreamFL: A Bayesian-enhanced multi-scale federated learning framework for dynamic data streams with uncertainty qualification. Expert Systems with Applications, Article 129882, accepted on 24 September 2025. Elsevier (DOI: 10.1016/j.eswa.2025.129882).
  3. Zhangqian Wu, Wei Song, Xiaoyan Zhao, Yinan Guo, and Shengxiang Yang. Distributed swarm attention learning optimizer with sensitive search transition for large-scale optimization. IEEE Transactions on Emerging Topics in Computational Intelligence, published online first: 29 September 2025. IEEE Press (DOI: 10.1109/TETCI.2025.3603999).
  4. Zhanglu Hou, Juan Zou, Yizhang Xia, Linshan Gao, Yuan Liu, and Shengxiang Yang. An improved reference point-based evolutionary algorithm for dynamic multi-objective optimization with preferences. IEEE Transactions on Emerging Topics in Computational Intelligence, published online first: 29 September 2025. IEEE Press (DOI: 10.1109/TETCI.2025.3611285).
  5. Feng Wang, Jinsong Xie, Fanshu Liao, Yixuan Li, Yinan Guo, Shengxiang Yang, and Aimin Zhou. A generative adversarial network based prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, published online first: 17 September 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3611014).
  6. Yaru Hu, Zhi Zheng, Junwei Ou, Yanjie Song, Jinhua Zheng, Juan Zou, Ponnuthurai Nagaratnam Suganthan, and Shengxiang Yang. A learning algorithm based on similarity identification and knowledge transfer for dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation, published online first: 12 August 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3597615).
  7. Mai Peng, Changhe Li, Junchen Wang, Xinye Cai, Sanyou Zeng, and Shengxiang Yang. A solution space partitioning based multi-population method for dynamic optimization. IEEE Transactions on Evolutionary Computation, published online first: 11 August 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3597453).
  8. Yiya Diao, Changhe Li, Sanyou Zeng, Xinye Cai, Shengxiang Yang, and Carlos A. Coello Coello. Nearest-better network for visualizing and analyzing combinatorial optimization problems: A potential unified tool. IEEE Transactions on Evolutionary Computation, published online first: 8 August 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3597078).
  9. Qian Bao, Maocai Wang, Shengxiang Yang, Guangming Dai, and Xiaoyu Chen. A coevolutionary response framework for dynamic constrained multi-objective optimization problems. IEEE Transactions on Evolutionary Computation, published online first: 6 August 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3595410).
  10. Shouyong Jiang, Yong Wang, Yaru Hu, Qingyang Zhang, and Shengxiang Yang. Dynamic multi-objective optimisation based on vector autoregressive evolution. IEEE Transactions on Evolutionary Computation, published online first: 14 May 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3570116).
  11. Juan Zou, Zhanglu Hou, Shouyong Jiang, Shengxiang Yang, Gan Ruan, Yizhang Xia, and Yuan Liu. Knowledge transfer with mixture model in dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation, published online first: 2 May 2025. IEEE Press (DOI: 10.1109/TEVC.2025.3566481).
  12. Yiya Diao, Changhe Li, Sanyou Zeng, Shengxiang Yang, and Carlos A. Coello Coello. Nearest-better network for fitness landscape analysis of continuous optimization problems. IEEE Transactions on Evolutionary Computation, published online first: 18 October 2024. IEEE Press (DOI: 10.1109/TEVC.2024.3478825).
  13. Wei Song, Shaocong Liu, Hongbin Yu, Yinan Guo, and Shengxiang Yang. Multi-region trend prediction strategy with online sequential extreme learning machine for dynamic multi-objective optimization. IEEE Transactions on Emerging Topics in Computational Intelligence, published online first: 7 August 2024. IEEE Press (DOI: 10.1109/TETCI.2024.3437166).
  14. Jian Feng, Shaoning Liu, Shengxiang Yang, Jun Zheng, and Qi Xiao. A coevolutionary algorithm with detection and supervision strategy for constrained multiobjective optimization. IEEE Transactions on Evolutionary Computation, published online first: 19 June 2024. IEEE Press (DOI: 10.1109/TEVC.2024.3416552).
  15. Qi Yan, Hongfeng Wang, and Shengxiang Yang. A learning-assisted bi-population evolutionary algorithm for distributed flexible job-shop scheduling with maintenance decisions. IEEE Transactions on Evolutionary Computation, published online first: 13 May 2024. IEEE Press (DOI: 10.1109/TEVC.2024.3400043).
  16. 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).
  17. Yaru Hu, Sitong Wang, Junwei Ou, Zhenlin Mei, Juan Zou, and Shengxiang Yang. Region-aware prediction strategy based on shared points and multiple scales for dynamic multi-objective optimization. Expert Systems with Applications, 298, Part B, Article 129642, March 2026. Elsevier (DOI: 10.1016/j.eswa.2025.129642).
  18. Guoyu Chen, Yinan Guo, Xiao Yang, Tianbing Ma, Shengxiang Yang, and Liang Yuan. Dynamic multiobjective evolutionary algorithm based on a knee point driven Gaussian model. Expert Systems with Applications, 297, Part A, Article 129325, February 2026. Elsevier (DOI: 10.1016/j.eswa.2025.129325).
  19. Hao Liu, Jinhua Zheng, Yaru Hu, Xiaozhong Yu, Junwei Ou, Juan Zou, and Shengxiang Yang. A strategy cooperative algorithm based on state-awareness for large-scale multi-objective optimization. Swarm and Evolutionary Computation, 99, Article 102165, December 2025. Elsevier (DOI: 10.1016/j.swevo.2025.102165).
  20. Jiazheng Li, Yuan Liu, Jiazheng Li, Juan Zou, Shuyi Liu, and Shengxiang Yang. Multi-agent cooperation-based bi-criteria evolutionary many-objective optimization. Applied Soft Computing, 185, Part A, Article 113865, December 2025. Elsevier (DOI: 10.1016/j.asoc.2025.113865).
  21. Wei Zhang, Wen Ma, Shengxiang Yang, Shengzong Chen, and Jihui Zhang. An improved adaptive large neighborhood search for the home health care routing and scheduling problem with multiple mixed time windows. Information Sciences, 719, Article 122458, November 2025. Elsevier (DOI: 10.1016/j.ins.2025.122458).
  22. Shaoning Liu, Jian Feng, Shengxiang Yang, and Jun Zheng. A population game-based knowledge transfer strategy for constrained multi-objective optimization. Swarm and Evolutionary Computation, 98, Article 102146, October 2025. Elsevier (DOI: 10.1016/j.swevo.2025.102146).
  23. Shaoning Liu, Jian Feng, Shengxiang Yang, Huaguang Zhang, Jun Zheng, and Yu Yao. A knowledge transfer-based strategy for constrained multiobjective optimization. Swarm and Evolutionary Computation, 98, Article 102111, October 2025. Elsevier (DOI: 10.1016/j.swevo.2025.102111).
  24. Qingyang Zhang, Xueliang Fu, Shengxiang Yang, Shouyong Jiang, Miqing Li, and Zedong Zheng. Solving dynamic multi-objective engineering design problems via fuzzy c-means prediction algorithm. Swarm and Evolutionary Computation, 98, Article 102057, October 2025. Elsevier (DOI: 10.1016/j.swevo.2025.102057).
  25. Yongkang Xing, Shengxiang Yang, Conor Fahy, Tracy Harwood, and Jethro Shell. Capturing the past, shaping the future: A scope review of photogrammetry in cultural building heritage. Electronics, 14(18), Article 3666, September 2025. MDPI (DOI: 10.3390/electronics14183666).
  26. Yuxuan Song, Yue Xu, Dechang Pi, and Shengxiang Yang. Competitive many-task differential evolution with reinforcement learning and meta-knowledge transfer. Knowledge-Based Systems, 326, Article 113931, September 2025. Elsevier (DOI: 10.1016/j.knosys.2025.113931).
  27. Jiawen Deng, Jihui Zhang, and Shengxiang Yang. A coevolutionary Q-learning-based memetic algorithm for distributed assembly heterogeneous flexible flowshop scheduling. Expert Systems with Applications, 288, Article 128198, September 2025. Elsevier (DOI: 10.1016/j.eswa.2025.128198).
  28. Jing Sun, Yaoguo Dang, Shengxiang Yang, Junjie Wang, and Ying Cai. A grey incidence model with fractional cumulative time delay effects and its applications. Applied Mathematical Modelling, 145, Article 116144, September 2025. Elsevier. (DOI: 10.1016/j.apm.2025.116144).
  29. Yinan Guo, Jiayang Pu, Jiale He, Jianjiao Ji, and Shengxiang Yang. Adaptive stochastic configuration network based on online active learning for evolving data streams. Information Sciences, 711, Article 122113, September 2025. Elsevier (DOI: 10.1016/j.ins.2025.122113).
  30. Dunwei Gong, Miao Rong, Na Hu, Yan Wang, Witold Pedrycz, and Shengxiang Yang. A prediction and weak coevolution-based dynamic constrained multi-objective optimization. IEEE Transactions on Evolutionary Computation, 29(4):1328-1342, August 2025. IEEE Press (DOI: 10.1109/TEVC.2024.3418470).
  31. Zhanglu Hou, Jialu Ye, Yizhang Xia, Yibin Gong, Juan Zou, and Shengxiang Yang. A decomposition framework with dual populations and dual stages for constrained multi-objective optimization. Swarm and Evolutionary Computation, 97, Article 102030, August 2025. Elsevier (DOI: 10.1016/j.swevo.2025.102030).
  32. Yang Chen, Dechang Pi, Shengxiang Yang, Yue Xu, Bi Wang, Qin Shuo, and Yintong Wang. A dynamic optimization framework for computation rate maximization in UAV-assisted mobile edge computing. IEEE Transactions on Vehicular Technology, 74(7): 11395-11409, July 2025. IEEE Press (DOI: 10.1109/TVT.2025.3546026).
  33. Qi Deng, Juan Zou, Shengxiang Yang, Yuan Liu, Fan Yu, Tianbin Xie, and Jinhua Zheng. A niching-based nondominated sorting for multimodal multiobjective optimization with local Pareto fronts. Applied Soft Computing, 179, Article 113223, July 2025. Elsevier (DOI: 10.1016/j.asoc.2025.113223).
  34. Biao Xu, Gejie Rang, Ruijie Xie, Wenji Li, Dunwei Gong, Zhun Fan, Shengxiang Yang, and Jie He. A prediction approach based on long short-term memory networks for dynamic multi-objective optimization. Expert Systems with Applications, 283, Article 127792, July 2025. Elsevier (DOI: 10.1016/j.eswa.2025.127792).
  35. Qi Yan, Hongfeng Wang, Shengxiang Yang, and Yaping Fu. Self-learning brainstorm optimization for synchronization of operations and maintenance toward dual resource-constrained flexible job shops. Applied Soft Computing, 177, Article 113230, June 2025. Elsevier (DOI: 10.1016/j.asoc.2025.113230).
  36. Zeyin Guo, Lixin Wei, Xin Li, Shengxiang Yang, and Jinlu Zhang. A variable window multi-interval rescheduling optimization algorithm for dynamic flexible job shop problem. Applied Soft Computing, 176, Article 113157, May 2025. Elsevier (DOI: 10.1016/j.asoc.2025.113157).
  37. Kun Chen, Shengxiang Yang, Dingbang Luh, Zihao Chen, Honghua Ai, and Yi An. Gamification as an innovative approach for the assessment of procedural knowledge. Electronics, 14(8), Article 1573, April 2025. MDPI (DOI: 10.3390/electronics14081573).
  38. Ho Yan Kwan, Jethro Shell, Conor Fahy, Shengxiang Yang, and Yongkang Xing. Integration of large language models in remote healthcare: Current applications, challenges, and future prospects. Systems, 13(4), Article 281, April 2025. MDPI (DOI: 10.3390/systems13040281).
  39. Likai Wang, Qingyang Zhang, Shengxiang Yang, and Yongquan Dong. Multi-strategy grey wolf optimization algorithm for global optimization and engineering applications. Journal of Systems Science and Systems Engineering, 34(2): 203-230, April 2025. Springer (DOI: 10.1007/s11518-024-5622-z).
  40. Jinze Liu, Jian Feng, Huaguang Zhang, and Shengxiang Yang. A novel multi-level hierarchy optimization algorithm for inner detector speed control. Neuralcomputing, 627, Article 129517, April 2025. Elsevier (DOI: 10.1016/j.neucom.2025.129715).
  41. Yiya Diao, Changhe Li, Junchen Wang, Sanyou Zeng, and Shengxiang Yang. Bridging the gap between theory and practice: Fitness landscape analysis of real-world problems with nearest-better network. Information, 16(3), Article 190, March 2025. MDPI (DOI: 10.3390/info16030190).
  42. 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, 57(3): 786-812, March 2025. Taylor & Francis (DOI: 10.1080/0305215X.2024.2335284).
  43. Fengxia Wang, Min Huang, Shengxiang Yang, and Xingwei Wang. A constrained multimodal multi-objective evolutionary algorithm based on adaptive epsilon method and two-level selection. Swarm and Evolutionary Computation, 93, Article 101845, March 2025. Elsevier (DOI: 10.1016/j.swevo.2025.101845).
  44. Yaru Hu, Huibing Wang, Junwei Ou, Juan Zou, Jinhua Zheng, and Shengxiang Yang. Dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach. Expert Systems with Applications, 263, Article 125765, March 2025. Elsevier (DOI: 10.1016/j.eswa.2024.125765).
  45. Yao Huang, Yinan Guo, Guoyu Chen, Hong Wei, Xiaoxiao Zhao, Shengxiang Yang, and Shirong Ge. Q-learning assisted optimization method for low-carbon scheduling of open-pit mine trucks. Swarm and Evolutionary Computation, 92, Article 101778, February 2025. Elsevier (DOI: 10.1016/j.swevo.2024.101778).
  46. Jie Chen, Shengxiang Yang, Conor Fahy, Zhu Wang, Yinan Guo, and Yingke Chen. Online sparse representation clustering for evolving data streams. IEEE Transactions on Neural Networks and Learning Systems, 36(1): 525-539, January 2025. IEEE Press (DOI: 10.1109/TNNLS.2023.3325556).
  47. Qi Deng, Yuan Liu, Shengxiang Yang, Juan Zou, Xijun Li, Yizhang Xia, and Jinhua Zheng. A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization. Applied Soft Computing, 169, Article 112516, January 2025. Elsevier (DOI: 10.1016/j.asoc.2024.112516).
  48. Haiyan Jin, Rui Ru, Lei Cai, Jinhao Meng, Bin Wang, Jichang Peng, and Shengxiang Yang. A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in Lithium-ion batteries. Applied Energy, 377, Part D, Article 124629, January 2025. Elsevier (DOI: 10.1016/j.apenergy.2024.124629).
  49. Yiya Diao, Changhe Li, Sanyou Zeng, and Shengxiang Yang. Nearest-better network assisted fitness landscape analysis of contaminant source identification in water distribution network. Data, 9(12), Article 142, December 2024. MDPI (DOI: 10.3390/data9120142).
  50. 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, 39(4): 2299-2311, December 2024. IEEE Press (DOI: 10.1109/TEC.2024.3385093).
  51. Shiting Wang, Jinhua Zheng, Yingjie Zou, Yuan Liu, Juan Zou, and Shengxiang Yang. A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization. Swarm and Evolutionary Computation, 91, Article 101752, December 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101752).
  52. Hai Xia, Changhe Li, Qingshan Tan, Sanyou Zeng, and Shengxiang Yang. Learning to search promising regions by space partitioning for evolutionary methods. Swarm and Evolutionary Computation, 91, Article 101726, December 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101726).
  53. Yaru Hu, Jiankang Peng, Junwei Ou, Yana Li, Jinhua Zheng, Juan Zou, Shouyong Jiang, Shengxiang Yang, and Jun Li. The IGD-based prediction strategy for dynamic multi-objective optimization. Swarm and Evolutionary Computation, 91, Article 101713, December 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101713).
  54. Ellen Knight, Heiko Balzter, Tom Breeze, Julia Brettschneider, Robbie Girling, Alex Hagen-Zanker, Mike Image, Colin Johnson, Christopher Lee, Andrew Lovett, Sergei Petrovskii, Alexa Varah, Mick Whelan, Shengxiang Yang, and Emma Gardner. Adapting genetic algorithms for multifunctional landscape decisions: a theoretical case study on wild bees and farmers in the UK. Methods in Ecology and Evolution, 15(11): 2153-2167, November 2024. John Wiley & Sons (DOI: 10.1111/2041-210X.14424).
  55. Wei Song, Zhi Liu, Shaocong Liu, Xiaofeng Ding, Yinan Guo, and Shengxiang Yang. Particle search control network for dynamic optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 54(11): 6961-6976, November 2024. IEEE Press (DOI: 10.1109/TSMC.2024.3448453).
  56. Yinan Guo, Zhiji Zheng, Jiayang Pu, Botao Jiao, Dunwei Gong, and Shengxiang Yang. Robust online active learning with cluster-based local drift detection for unbalanced imperfect data. Applied Soft Computing, 165, Article 112051, November 2024. Elsevier (DOI: 10.1016/j.asoc.2024.112051).
  57. 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, 28(5): 1529-1543, October 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3316723).
  58. 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, 28(5): 1381-1395, October 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3313689).
  59. 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, 54(9): 5529-5542, September 2024. IEEE Press. (DOI: 10.1109/TCYB.2024.3364375).
  60. Shouyong Jiang, Jinglei Guo, Yong Wang, and Shengxiang Yang. Evolutionary multi/many-objective optimisation via bilevel decomposition. IEEE/CAA Journal of Automatica Sinica, 11(9): 1974-1987, September 2024. IEEE Press (DOI: 10.1109/JAS.2024.124515).
  61. Zhipan Li, Huigui Rong, Shengxiang Yang, Xu Yang, and Yupeng Huang. A dual-population coevolutionary algorithm for balancing convergence and diversity in the decision space in multimodal multi-objective optimization. Applied Soft Computing, 162, Article 111770, September 2024. Elsevier (DOI: 10.1016/j.asoc.2024.111770).
  62. Yuan Liu, Jiazheng Li, Juan Zou, Zhanglu Hou, Shengxiang Yang, and Jinhua Zheng. Continuous variation operator configuration for decomposition-based evolutionary multi-objective optimization. Swarm and Evolutionary Computation, 89, Article 101644, August 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101644).
  63. Wang Che, Jinhua Zheng, Yaru Hu, Juan Zou, and Shengxiang Yang. Dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement. Swarm and Evolutionary Computation, 89, Article 101639, August 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101639).
  64. Xueqing Wang, Jinhua Zheng, Zhanglu Hou, Yuan Liu, Juan Zou, Yizhang Xia, and Shengxiang Yang. A novel preference-driven evolutionary algorithm for dynamic multi-objective problems. Swarm and Evolutionary Computation, 89, Article 101638, August 2024. Elsevier (DOI: 10.1016/j.swevo.2024.101638).
  65. 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).
  66. Jiawen Deng, Jihui Zhang, and Shengxiang Yang. Optimizing electric vehicle routing with nonlinear charging and time windows using improved differential evolution algorithm. Cluster Computing, 27(4): 5423-5458, July 2024. Springer (DOI: 10.1007/s10586-023-04243-z).
  67. Hui Yuan, Raouf Hamzaoui, Ferrante Neri, Shengxiang Yang, Xin Lu, Linwei Zhu and Yun Zhang. Optimized quantization parameter selection for video-based point cloud compression. Frontiers in Signal Processing, 4, Article 1385287, July 2024. Frontiers Media S.A. (DOI: 10.3389/frsip.2024.1385287).
  68. Juan Zou, Li Tang, Yuan Liu, Shengxiang Yang, and Shiting Wang. A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization. Information Sciences, 674, Article 120719, July 2024. Elsevier (DOI: 10.1016/j.ins.2024.120719).
  69. 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, 73(2): 1341-1352, June 2024. IEEE Press (DOI: 10.1109/TR.2023.3324701).
  70. Qingda Chen, Jinliang Ding, Gary G. Yen, Shengxiang Yang, and Tianyou Chai. Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments. IEEE Transactions on Evolutionary Computation, 28(3): 763-777, June 2024. IEEE Press (DOI: 10.1109/TEVC.2023.3241762).
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  255. Hongfeng Wang, Shengxiang Yang, Wai Hung Ip, and Dingwei 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).
  256. Changhe Li, Shengxiang Yang, and Trung Thanh 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).
  257. Lili Liu, Shengxiang Yang, and Dingwei 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).
  258. Sadaf Naseem Jat and Shengxiang 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).
  259. Renato Tinos and Shengxiang 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++).
  260. Michalis Mavrovouniotis and Shengxiang 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).
  261. Renato Tinos and Shengxiang 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).
  262. Iulia Comsa, Crina Grosan, and Shengxiang 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).
  263. Hui Cheng and Shengxiang 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++).
  264. Xingguang Peng, Xiaoguang Gao, and Shengxiang 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).
  265. Shengxiang Yang and Sadaf Naseem 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).
  266. Shengxiang Yang and Changhe 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++).
  267. Lili Liu, Shengxiang Yang, and Dingwei 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).
  268. Hongfeng Wang, Shengxiang Yang, Wai Hung Ip, and Dingwei 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).
  269. Hui Cheng and Shengxiang 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).
  270. Hui Cheng, Xingwei Wang, Shengxiang Yang, Min Huang, and Jiannong 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).
  271. Shengxiang Yang, Dingwei Wang, Tianyou Chai, and Graham 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).
  272. Shengxiang Yang, Hui Cheng, and Fang 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++).
  273. Hongfeng Wang, Shengxiang Yang, Wai Hung Ip, and Dingwei 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).
  274. Henato Richter and Shengxiang 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).
  275. Hongfeng Wang, Dingwei Wang, and Shengxiang 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).
  276. Hui Cheng, Jiannong Cao, Xingwei Wang, Sajal K. Das, and Shengxiang 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).
  277. Hui Cheng, Xingwei Wang, Shengxiang Yang, and Min 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).
  278. Shengxiang Yang and Xin 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++).
  279. Shengxiang 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++).
  280. Renato Tinos and Shengxiang 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).
  281. Shengxiang Yang and Renato 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++).
  282. Shengxiang Yang and Xin 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).
  283. Shengxiang Yang. Adaptive group mutation for tackling deception in genetic search. WSEAS Transactions on Systems, 3(1): 107-112, January 2004 (PDF File).
  284. Shengxiang Yang and Dingwei 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).
  285. Shengxiang ang and Dingwei 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, 39(11): 3839-3847, November 2024 (DOI: 10.13195/j.kzyjc.2023.0354).
  2. Kangyu Xu, Yuan Liu, Miqing Li, Shengxiang Yang, Juan Zou, and Jinhua 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. Jinhua Zheng, Jiangnan Dong, Gan Ruan, Juan Zou, Shengxiang 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. Xiaodong Zhang and Shengxiang 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. Hongfeng Wang, Dingwei Wang, and Shengxiang 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. Shengxiang Yang and Dingwei 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. Shengxiang Yang and Dingwei 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. Shengxiang Yang and Dingwei 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. Shengxiang Yang and Dingwei 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. Danial Yazdani, Wenjian Luo, and Shengxiang Yang. Guest editorial: Evolutionary dynamic optimization. IEEE Transactions on Evolutionary Computation, 29(5): 1-4, October 2025, IEEE Press (DOI: 10.1109/TEVC.2025.3613238).
  2. Hui Cheng, Shengxiang Yang, Xin Yao, and Mengjie 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).
  3. Ferrante Neri and Shengxiang 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).
  4. Shengxiang Yang, Yew-Soon Ong, and Yaochu 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. Changhe Li, Sanyou Zeng, and Shengxiang 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. Michalis Mavrovouniotis and Shengxiang 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).
  3. Conor Fahy and Shengxiang 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).
  4. Michalis Mavrovouniotis and Shengxiang 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).
  5. Shengxiang Yang, Trung Thanh Nguyen, and Changhe Li. Evolutionary dynamic optimization: test and evaluation environments. In Shengxiang Yang and Xin 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).
  6. Trung Thanh Nguyen, Shengxiang Yang, Juergen Branke, and Xin Yao. Evolutionary dynamic optimization: methodologies. In Shengxiang Yang and Xin 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).
  7. Changhe Li and Shengxiang Yang. A comparative study on particle swarm optimization in dynamic environments. In Shengxiang Yang and Xin 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).
  8. Hongfeng Wang and Shengxiang Yang. Memetic algorithms for dynamic optimization problems. In Shengxiang Yang and Xin 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).
  9. Renato Tinos and Shengxiang Yang. Analyzing evolutionary algorithms for dynamic optimization problems based on the dynamical systems approach. In Shengxiang Yang and Xin 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).
  10. Iulia Maria Comsa, Crina Grosan, and Shengxiang Yang. Dynamics in the multi-objective subset sum: analysing the behaviour of population based algorithms. In Shengxiang Yang and Xin 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).
  11. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problem. In Shengxiang Yang and Xin 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).
  12. Hui Cheng and Shengxiang Yang. Genetic algorithms for dynamic routing problems in mobile ad hoc networks. In Shengxiang Yang and Xin 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).
  13. Xingguang Peng, Shengxiang Yang, Demin Xu, and Xiaoguang Gao. Evolutionary algorithms for the multiple unmanned aerial combat vehicles anti-ground attack problem in dynamic environments. In Shengxiang Yang and Xin 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).
  14. Hendrik Richter and Shengxiang 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).
  15. Yang Yan, Shengxiang Yang, Dazhi Wang, and Dingwei 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).
  16. Hui Cheng, Xingwei Wang, Min Huang, and Shengxiang 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).
  17. Shengxiang Yang. Explicit memory schemes for evolutionary algorithms in dynamic environments. In Shengxiang Yang, Yew-Soon Ong, and Yaochu 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).
  18. Renato Tinos and Shengxiang Yang. Genetic algorithms with self-organizing behaviour in dynamic environments. In Shengxiang Yang, Yew-Soon Ong, and Yaochu 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).
  19. Shengxiang 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).
  20. Shengxiang 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).
  21. Shengxiang 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. Jiajun Wang, Changhe Li, and Shengxiang Yang. Deep reinforcement learning-based dynamic multi-objective truck scheduling with real-time ore blending for open-pit mining. Proceedings of the 2025 China Automation Congress, pp. 1-6, 2025. IEEE Press.
  2. Juan Zou, Yu Li, Qi Deng, Tianbin Xie, Shengxiang Yang, and Jinhua Zheng. Simulated annealing-based evolutionary algorithm for constrained multimodal multiobjective optimization. Proceedings of the 2025 Genetic and Evolutionary Computation Conference (GECCO ’25 Companion), pp. 463-466, 2025. ACM Press (DOI: 10.1145/3712255.3726752).
  3. Mengli Shan, Changhe Li, Xiaobo Liu, Mai Peng, Michalis Mavrovouniotis, and Shengxiang Yang. Feasible regions identification based on historical solutions for constrained optimization problems. Proceedings of the 2025 IEEE Congress on Evolutionary Computation, pp. 1-8, 2025. IEEE Press (DOI: 10.1109/CEC65147.2025.11043100).
  4. Xinzhuo Wang, Chang Liu, Rui Wang, Zhenghao Yu, and Shengxiang Yang. Improved genetic algorithm using reinforcement learning to solve the re-entrant flexible flow shop scheduling problem. Proceedings of the 2025 IEEE Congress on Evolutionary Computation, pp. 1-8, 2025. IEEE Press (DOI: 10.1109/CEC65147.2025.11043111).
  5. Rui Wang, Chang Liu, Xinzhuo Wang, Shengxiang Yang, and Yaqi Hou. A multi-action deep reinforcement learning based on BiLSTM for flexible job shop scheduling problem with tight time. Proceedings of the 8th International Conference on Computer Science and Artificial Intelligence (CSAI 2024), pp. 318-326, 2024. ACM Press (DOI: 10.1145/3709026.3709038).
  6. Zihao Zhang, Changhe Li, and Shengxiang Yang. Reinforcement learning-enhanced dynamic multiobjective evolutionary algorithm for cooperative shovel scheduling in open-pit mining. Proceedings of the 2024 China Automation Congress, pp. 5264-5269, 2024. IEEE Press. (DOI: 10.1109/CAC63892.2024.10864693).
  7. Juan Zou, Tianbin Xie, Qi Deng, Xiaozhong Yu, Shengxiang Yang, and Jinhua Zheng. Differential evolution based on local grid search for multimodal multiobjective optimization with local Pareto fronts. Proceedings of the 2024 Genetic and Evolutionary Computation Conference (GECCO ’24 Companion), pp. 399-402, 2024. ACM Press (DOI: 10.1145/3638530.3654235).
  8. Xueqing Wang, Jinhua Zheng, Juan Zou, Zhanglu Hou, Yuan Liu, and Shengxiang Yang. A dynamic preference-driven evolutionary algorithm for solving dynamic multi-objective problems. Proceedings of the 2024 Genetic and Evolutionary Computation Conference (GECCO ’24 Companion), pp. 379-382, 2024. ACM Press (DOI: 10.1145/3638530.3654148).
  9. Saneet Fulsunder, Saidu Umar, Aboozar Taherkhani, Chang Liu, and Shengxiang Yang. Hand gesture recognition using a multi-modal deep neural network. In: Z. Shi, J. Torresen, and S. Yang (eds), Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704, pp. 189-203, 2024. Springer (DOI: 10.1007/978-3-031-57919-6_14).
  10. Stephen S. Aremu, Aboozar Taherkhani, Chang Liu, and Shengxiang Yang. 3D object reconstruction with deep learning. In: Z. Shi, J. Torresen, and S. Yang (eds), Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704, pp. 161-175, 2024. Springer (DOI: 10.1007/978-3-031-57919-6_12).
  11. 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. In: Z. Shi, J. Torresen, and S. Yang (eds), Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704, pp. 150-160, 2024. Springer (DOI: 10.1007/978-3-031-57919-6_11).
  12. Anju Yang, Yuan Liu, Juan Zou, and Shengxiang Yang. Decomposed multi-objective method based on Q-learning for solving multi-objective combinatorial optimization. In: L. Pan, Y. Wang, and J. Lin (eds), Bio-Inspired Computing: Theories and Applications. BIC-TA 2023. Communications in Computer and Information Science, vol 2061, pp. 59-73, 2024. Springer, Singapore (DOI: 10.1007/978-981-97-2272-3_5).
  13. Conor Fahy and Shengxiang 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).
  14. Junxiang Qiu, Changhe Li, and Shengxiang 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. 5370-5375, 2023. IEEE Press (DOI: 10.1109/CAC59555.2023.10451038).
  15. Mengting Ao, Changhe Li, and Shengxiang 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).
  16. Yaqi Ti, Changhe Li, and Shengxiang 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).
  17. Qingshan Tan, Changhe Li, Sanyou Zeng, and Shengxiang 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).
  18. Yiya Diao, Changhe Li, Sanyou Zeng, and Shengxiang 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).
  19. Shiting Wang, Jinhua Zheng, Juan Zou, Yuan Liu, Shengxiang Yang, and Yingjie 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).
  20. Jinhua Zheng, Kaixi Yang, Juan Zou, and Shengxiang Yang. Combining state detection with knowledge transfer for constrained multi-objective optimization. Proceedings of the IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 712-719, 2022. IEEE Press (DOI: 10.1109/ICTAI56018.2022.00110).
  21. Hai Xia, Changhe Li, Sanyou Zeng, Qingshan Tan, J. Wang, and Shengxiang Yang. Learning to search promising regions by a Monte-Carlo tree model. Proceedings of the 2022 IEEE Congress on Evolutionary Computation, pp. 1-8, 2022. IEEE Press (DOI: 10.1109/CEC55065.2022.9870281).
  22. Baojian Chen, Changhe Li, Sanyou Zeng, Shengxiang Yang, and Michalis Mavrovouniotis. An adapive evolutionary algorithm for bi-level multi-objective VRPs with real-time traffic conditions. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2021. IEEE Press (DOI: 10.1109/SSCI50451.2021.9659933).
  23. Hui Yuan, Raouf Hamzaoui, Ferrante Neri, Shengxiang Yang, and Tingting Wang. Global rate-distortion optimization of video-based point cloud compression with differential evolution. Proceedings of the IEEE 23rd International Workshop on Multimedia Signal Processing, pp. 1-6, 2021. IEEE Press (DOI: 10.1109/MMSP53017.2021.9733714 and Data Files).
  24. Hui Yuan, Raouf Hamzaoui, Ferrante Neri, Shengxiang Yang, and Tingting Wang. Model-based rate-distortion optimized video-based point cloud compression with differential evolution. Proceedings of the 11th International Conference on Image and Graphics, pp. 735-747, 2021. Springer (DOI: 10.1007/978-3-030-87355-4_61).
  25. Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar, Luis-Alexander Calvo-Valverde, Shengxiang Yang, Armaghan Moemeni, Davia Elizondo, Ezequiel Lopez-Rubio, and Miguel A. Molina-Cabello. Improving uncertainty estimations for mammogram classification using semi-supervised learning. Proceedings of the 2021 IEEE International Joint Conference on Neural Networks, pp. 1-8, 2021. IEEE Press. (DOI: 10.1109/IJCNN52387.2021.9533719).
  26. Hai Xia, Changhe Li, Sanyou Zeng, Qingshan Tan, Junchen Wang, and Shengxiang Yang. A reinforcement-learning-based evolutionary algorithm using solution space clustering for multimodal optimization problems. Proceedings of the 2021 IEEE Congress on Evolutionary Computation, pp. 1938-1945, 2021. IEEE Press (DOI: 10.1109/CEC45853.2021.9504896).
  27. Qingshan Tan, Changhe Li, Hai Xia, Sanyou Zeng, and Shengxiang Yang. A novel scalable framework for constructing dynamic multi-objective optimization problems. Proceedings of the 2021 IEEE Congress on Evolutionary Computation, pp. 111-118, 2021. IEEE Press (DOI: 10.1109/CEC45853.2021.9504961).
  28. Saul Calderon-Ramirez, Raghvendra Giri, Shengxiang Yang, Armaghan Moemeni, Mario Umana, David Elizondo, Jordina Torrents-Barrena, and Miguel A. Molina-Cabello. Dealing with scarce labelled data: Semi-supervised deep learning with mix match for Covid-19 detection using chest X-ray images. Proceedings of the 25th International Conference on Pattern Recognition, pp. 5294-5301, 2020. IEEE Press (DOI: 10.1109/ICPR48806.2021.9412946).
  29. Guangwu Cui, Ruimin Shen, Yingfeng Chen, Juan Zou, Shengxiang Yang, Changjie Fan, Jinhua Zheng. Reinforced evolutionary algorithms for game difficulty control. Proceedings of the 3rd International Conference on Algorithms, Computing and Artificial Intelligence, pp. 197-203, December 2020. ACM Press (DOI: 10.1145/3446132.3446165).
  30. Zedong Zheng and Shengxiang Yang. Particle swarm optimisation for scheduling electric vehicles with microgrids. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, pp. 1-7, 2020. IEEE Press (DOI: 10.1109/CEC48606.2020.9185853).
  31. Matthew Fox, Shengxiang Yang, and Fabio Caraffini. An experimental study of prediction methods in robust optimization over time. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, pp. 1-7, 2020. IEEE Press (DOI: 10.1109/CEC48606.2020.9185910).
  32. Wenjie Liu, Wenjian Luo, Xin Lin, Miqing Li, and Shengxiang Yang. An evolutionary approach to multiparty multiobjective optimization problems with common Pareto optimal solutions. Proceedings of the 2020 IEEE Congress on Evolutionary Computation, pp. 1-9, 2020. IEEE Press (DOI: 10.1109/CEC48606.2020.9185747).
  33. Ariana Bermudez, Saul Calderon-Ramirez, Trevor Thang, Pascal Tyrrell, Armaghan Moemeni, Shengxiang Yang, and Jordina Torrents-Barrena. A first glance to the quality assessment of dental photostimulable phosphor plates with deep learning. Proceedings of the 2020 IEEE International Joint Conference on Neural Networks, pp. 1-6, 2020. IEEE Press (DOI: 10.1109/IJCNN48605.2020.9206779).
  34. Jinglei Guo, Miaomiao Shao, Shouyong Jiang, and Shengxiang Yang. An improved multiobjective optimization evolutionary algorithm based on decomposition with hybrid penalty scheme. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 165-166, 2020. Association for Computing Machinery, New York, NY, USA (DOI: 10.1145/3377929.3389958).
  35. Jinhua Zheng, Tian Chen, Huipeng Xie, and Shengxiang Yang. An improved memory prediction strategy for dynamic multiobjective optimization. Proceedings of the 5th International Conference on Computational Intelligence and Applications, pp. 166-171, 2020. IEEE Press (DOI: 10.1109/ICCIA49625.2020.00039).
  36. Xiaofang Wu, Sanyou Zeng, Changhe Li, and Shengxiang 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).
  37. Long Xiao, Changhe Li, Junchen Wang, Michalis Mavrovouniotis, Shengxiang Yang, and Xiaorong Dan. Modeling and evolutionary optimization for multi-objective vehicle routing problem with real-time traffic conditions. Proceedings of the 12th International Conference on Machine Learning and Computing, pp. 518-523, 2020. Association for Computing Machinery, New York, NY, USA (DOI: 10.1145/3383972.3384041).
  38. Yiya Diao, Changhe Li, Sanyou Zeng, Michalis Mavrovouniotis, and Shengxiang Yang. Memory-based multi-population genetic learning for dynamic shortest path problems. Proceedings of the 2019 IEEE Congress on Evolutionary Computation, pp. 2277-2284, 2019. IEEE Press (DOI: 10.1109/CEC.2019.8790211).
  39. Zedong Zheng and Shengxiang Yang. A two-layer optimisation management method for the microgrid with electric vehicles. Proceedings of the 2019 IEEE Congress on Evolutionary Computation, pp. 1079-1086, 2019. IEEE Press (DOI: 10.1109/CEC.2019.8790244).
  40. Zhanglu Hou, Shengxiang Yang, Juan Zou, Jinhua Zheng, Guo Yu, and Gan Ruan. A performance indicator for reference-point-based multiobjective evolutionary optimization. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, pp. 1571-1578, 2018. IEEE Press (DOI: 10.1109/SSCI.2018.8628834).
  41. Jianwei Zhou, Juan Zou, Shengxiang Yang, Gan Ruan, Junwei Ou, and Jinhua Zheng. An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, pp. 2148-2154, 2018. IEEE Press (DOI: 10.1109/SSCI.2018.8628655).
  42. Shouyong Jiang, Marcus Kaiser, Jinglei Guo, Shengxiang Yang, and Natalio Krasnogor. Less detectable environmental changes in dynamic multiobjective optimisation. Proceedings of the 2018 Genetic and Evolutionary Computation Conference, pp. 673-680, 2018. ACM Press (DOI: 10.1145/3205455.3205521).
  43. Muhanad Tahrir Younis, Shengxiang Yang, and Benjamin N. Passow. A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing. Proceedings of the 2018 IEEE Congress on Evolutionary Computation, pp. 1746-1753, 2018. IEEE Press (DOI: 10.1109/CEC.2018.8477765).
  44. Shouyong Jiang, Marcus Kaiser, Shuzhen Wan, Jinglei Guo, Shengxiang Yang, and Natalio Krasnogor. An empirical study of dynamic triobjective optimisation problems. Proceedings of the 2018 IEEE Congress on Evolutionary Computation, pp. 1369-1376, 2018. IEEE Press (DOI: 10.1109/CEC.2018.8477667).
  45. Darren M. Chitty, Shengxiang Yang, and Mario Gongora. Considering flexibility in the evolutionary dynamic optimisation of airport security lane schedules. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 1569-1576, 2017. IEEE Press (DOI: 10.1109/SSCI.2017.8285177).
  46. Michalis Mavrovouniotis, Mien Van and Shengxiang Yang. Pheromone modification strategy for the dynamic travelling salesman problem with weight changes. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 1577-1584, 2017. IEEE Press (DOI: 10.1109/SSCI.2017.8285229).
  47. Liuwei Fu, Juan Zou, Shengxiang Yang, Gan Ruan, Jinhua Zheng, and Zhongwei Ma. A proportion-based selection scheme for multi-objective optimization. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 2387-2393, 2017. IEEE Press (DOI: 10.1109/SSCI.2017.8285266).
  48. Conor Fahy, Shengxiang Yang, and Mario Gongora. Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters. Proceedings of the 2017 IEEE Congress on Evolutionary Computation, pp. 673-680, 2017. IEEE Press (DOI: 10.1109/CEC.2017.7969375).
  49. Darren M. Chitty, Shengxiang Yang, and Mario Gongora. Robustness and evolutionary dynamic optimisation of airport security schedules. Proceedings of 23rd International Conference on Soft Computing (MENDEL 2017), pp. 27-39, 2017. Springer (DOI: 10.1007/978-3-319-97888-8_3).
  50. Michalis Mavrovouniotis, Anastasia Ioannou, and Shengxiang Yang. Pre-scheduled colony size variation in dynamic environments. EvoApplications 2017: Applications of Evolutionary Computation, Part I, Lecture Notes in Computer Science, vol. 10199, pp. 177-189, 2017. Springer (DOI: 10.1007/978-3-319-55792-2_9).
  51. Muhanad Tahrir Younis, Shengxiang Yang, and Benjamin Passow Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing. EvoApplications 2017: Applications of Evolutionary Computation, Part II, Lecture Notes in Computer Science, vol. 10200, pp. 128-139, 2017. Springer (DOI: 10.1007/978-3-319-55849-3_12).
  52. Shouyong Jiang, Shengxiang Yang, and Miqing Li. On the use of hypervolume for diversity measurement of Pareto front approximations. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2016. IEEE Press (DOI: 10.1109/SSCI.2016.7850225).
  53. Darren M. Chitty, Mario Gongora, and Shengxiang Yang. Evolutionary dynamic optimisation of airport security lane schedules. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2016. IEEE Press (DOI: 10.1109/SSCI.2016.7849966).
  54. Jayne Eaton and Shengxiang Yang. Railway platform reallocation after dynamic perturbations using ant colony optimisation. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2016. IEEE Press (DOI: 10.1109/SSCI.2016.7849965).
  55. Renato Tinos and Shengxiang Yang. Artificially inducing environmental changes in evolutionary dynamic optimization. Proceedings of the 14th International Conference on Parallel Problems Solving from Nature (PPSN XIV), Lecture Notes in Computer Science, vol. 9921, pp. 225-236, 2016. Springer (DOI: 10.1007/978-3-319-45823-6_21).
  56. Shouyong Jiang and Shengxiang Yang. Convergence versus diversity in multiobjective optimization. Proceedings of the 14th International Conference on Parallel Problems Solving from Nature (PPSN XIV), Lecture Notes in Computer Science, vol. 9921, pp. 984-993, 2016. Springer (DOI: 10.1007/978-3-319-45823-6_92).
  57. Jia-Peng Li, Yong Wang, Shengxiang Yang and Zixing Cai. A comparative study of constraint-handling techniques in evolutionary constrained multiobjective optimization. Proceedings of the 2016 IEEE Congress on Evolutionary Computation, pp. 4175-4182, 2016. IEEE Press (DOI: 10.1109/CEC.2016.7744320).
  58. Zhi-Zhong Liu, Yong Wang, Shengxiang Yang and Zixing Cai. Differential evolution with a two-stage optimization mechanism for numerical optimization. Proceedings of the 2016 IEEE Congress on Evolutionary Computation, pp. 3170-3177, 2016. IEEE Press (DOI: 10.1109/CEC.2016.7744190).
  59. Shouyong Jiang, Shengxiang Yang, and Jinglei Guo. An adaptive penalty-based boundary intersection approach for multiobjective evolutionary algorithm based on decomposition. Proceedings of the 2016 IEEE Congress on Evolutionary Computation, pp. 2145-2152, 2016. IEEE Press (DOI: 10.1109/CEC.2016.7744053).
  60. Michalis Mavrovouniotis and Shengxiang Yang. Empirical study on the effect of population size on MAX-MIN ant system in dynamic environments. Proceedings of the 2016 IEEE Congress on Evolutionary Computation, pp. 853-860, 2016. IEEE Press (DOI: 10.1109/CEC.2016.7743880).
  61. Michalis Mavrovouniotis and Shengxiang Yang. Direct memory schemes for population-based incremental learning in cyclically changing environments. EvoApplications 2016: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9598, pp. 233-247, 2016. Springer. (DOI: 10.1007/978-3-319-31153-1_16). This paper was nominated to the Best Paper Award for EvoApplications 2016.
  62. Michalis Mavrovouniotis and Shengxiang Yang. Population-based incremental learning with immigrants schemes in changing environments. Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence, pp. 1444-1451, 2015. IEEE Press (DOI: 10.1109/SSCI.2015.205).
  63. Shouyong Jiang and Shengxiang Yang. A fast strength pareto evolutionary algorithm incorporating predefined preference information. Proceedings of the 15th UK Workshop on Computational Intelligence, pp. 1-8, 2015. IEEE Press.
  64. Shouyong Jiang and Shengxiang Yang. Approximating multiobjective optimization problems with complex pareto fronts. Proceedings of the 15th UK Workshop on Computational Intelligence, pp. 1-8, 2015. IEEE Press.
  65. Jun Qi, Liming Chen, Wolfgang Leister, and Shengxiang Yang. Towards knowledge driven decision support for personalized home-based self-management of chronic diseases. Proceeding of the 2015 Smart World Congress, pp. 1724-1729, 2015. IEEE Press (DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.313).
  66. Michalis Mavrovouniotis, Felipe M. Muller, and Shengxiang Yang. An ant colony optimization based memetic algorithm for the dynamic travelling salesman problem. Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 49-56, 2015. ACM Press (DOI: 10.1145/2739480.2754651). This paper was nominated to the Best Paper Award of the ACO-SI Track at the 2015 Genetic and Evolutionary Computation Conference (GECCO-2015).
  67. Miqing Li, Shengxiang Yang, and Xiaohui Liu. A performance comparison indicator for Pareto front approximations in many-objective optimization. Proceedings of the 17th Annual Conference on Genetic and Evolutionary Computation, pp. 703-710, 2015. ACM Press (DOI: 10.1145/2739480.2754687).
  68. Michalis Mavrovouniotis, Ferrante Neri, and Shengxiang Yang. An adaptive local search algorithm for real-valued dynamic optimization. Proceedings of the 2015 IEEE Congress on Evolutionary Computation, pp. 1388-1395, 2015. IEEE Press (DOI: 10.1109/CEC.2015.7257050).
  69. Michalis Mavrovouniotis and Shengxiang Yang. Applying ant colony optimization to dynamic binary-encoded problems. EvoApplications 2015: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 9028, pp. 845-856, 2015. Springer (DOI: 10.1007/978-3-319-16549-3_68).
  70. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization with self-adaptive evaporation rate in dynamic environments. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 47-54, 2014. IEEE Press (DOI: 10.1109/CIDUE.2014.7007866).
  71. Shouyong Jiang and Shengxiang Yang. A framework of scalable dynamic test problems for dynamic multi-objective optimization. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 32-39, 2014. IEEE Press (DOI: 10.1109/CIDUE.2014.7007864).
  72. Michalis Mavrovouniotis, Shengxiang Yang, and Xin Yao. Multi-colony ant algorithms for the dynamic travelling salesman problem. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 9-16, 2014. IEEE Press (DOI: 10.1109/CIDUE.2014.7007861).
  73. Jayne Eaton and Shengxiang Yang. Dynamic railway junction rescheduling using population based ant colony optimisation. Proceedings of the 14th UK Workshop on Computational Intelligence, pp. 1-8, 2014. IEEE Press (DOI: 10.1109/UKCI.2014.6930174).
  74. Shouyong Jiang and Shengxiang Yang. A benchmark generator for dynamic multi-objective optimization problems. Proceedings of the 14th UK Workshop on Computational Intelligence, pp. 1-8, 2014. IEEE Press (DOI: 10.1109/UKCI.2014.6930171).
  75. Miqing Li, Shengxiang Yang, and Xiaohui Liu. A test problem for visual investigation of high-dimensional multi-objective search. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 2140-2147, 2014. IEEE Press (DOI: 10.1109/CEC.2014.6900306 and Source Code in C). This paper was the winner of the 2014 IEEE Congress on Evolutionary Computation Best Student Paper Award.
  76. Michalis Mavrovouniotis and Shengxiang Yang. Elitism-based immigrants for ant colony optimization in dynamic environments: Adapting the replacement rate. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 1752-1759, 2014. IEEE Press (DOI: 10.1109/CEC.2014.6900482).
  77. Michalis Mavrovouniotis and Shengxiang Yang. Interactive and non-interactive hybrid immigrants schemes for ant algorithms in dynamic environments. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 1542-1549, 2014. IEEE Press (DOI: 10.1109/CEC.2014.6900481).
  78. Shouyong Jiang and Shengxiang Yang. An improved quantum-behaved particle swarm optimization based on linear interpolation. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pp. 769-775, 2014. IEEE Press (DOI: 10.1109/CEC.2014.6900354).
  79. Rui Hu, Shengxiang Yang, and Xiaochuan Luo. Ant colony optimization for scheduling walking beam reheating furnaces. Proceedings of the 11th World Congress on Intelligent Control and Automation, pp. 621-626, 2014. IEEE Press (DOI: 10.1109/WCICA.2014.7052786).
  80. Michalis Mavrovouniotis and Shengxiang Yang. Evolving neural networks using ant colony optimization with pheromone trail limits. Proceedings of the 13th UK Workshop on Computational Intelligence, pp. 16-23, 2013. IEEE Press (DOI: 10.1109/UKCI.2013.6651282).
  81. Michalis Mavrovouniotis and Shengxiang Yang. Genetic algorithms with adaptive immigrants for dynamic environments. Proceedings of the 2013 IEEE Congress on Evolutionary Computation, pp. 2130-2137, 2013. IEEE Press (DOI: 10.1109/CEC.2013.6557821).
  82. Weijian Kong, Tianyou Chai, Jinliang Ding, Xiuping Zheng, and Shengxiang Yang. A multiobjective particle swarm optimization for load scheduling in electric smelting furnaces. Proceedings of the 2013 IEEE Symposium on Computational Intelligence for Engineering Solutions, pp. 188-195, 2013. IEEE Press (DOI: 10.1109/CIES.2013.6611748).
  83. Michalis Mavrovouniotis and Shengxiang Yang. Adapting the pheromone evaporation rate in dynamic routing problems. EvoApplications 2013: Applications of Evolutionary Computation, Lecture Notes in Computer Science, vol. 7835, pp. 606-615, 2013. Springer (DOI: 10.1007/978-3-642-37192-9_61).
  84. Miqing Li, Shengxiang Yang, Xiaohui Liu, and Ruimin Shen. A comparative study on evolutionary algorithms for many-objective optimization. Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), Lecture Notes in Computer Science, vol. 7811, pp. 261-275, 2013. Springer (DOI: 10.1007/978-3-642-37140-0_22).
  85. Miqing Li, Shengxiang Yang, Xiaohui Liu, and Kang Wang. IPESA-II: Improved Pareto envelope-based selection algorithm II. Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013), Lecture Notes in Computer Science, vol. 7811, pp. 143-155, 2013. Springer (DOI: 10.1007/978-3-642-37140-0_14 and Source Code in C).
  86. Yefeng Liu, Tianyou Chai, S. Joe Qin, Quanke Pan, and Shengxiang Yang. Improved genetic algorithm for magnetic material two-stage multi-product production scheduling: A case study. Proceedings of the IEEE 51st Annaul Conference on Decision and Control (CDC), pp. 2521-2526, 2012. IEEE Press (DOI: 10.1109/CDC.2012.6426459).
  87. Zujian Wu, Shengxiang Yang, and David Gilbert. A hybrid approach to piece-wise modelling biochemical systems. Proceedings of the 12th International Conference on Parallel Problems Solving from Nature (PPSN XII), Part I, Lecture Notes in Computer Science, vol. 7491, pp. 519-528, 2012. Springer (DOI: 10.1007/978-3-642-32937-1_52).
  88. Michalis Mavrovouniotis, Shengxiang Yang, and Xin Yao. A benchmark generator for dynamic permutation-encoded problems. Proceedings of the 12th International Conference on Parallel Problems Solving from Nature (PPSN XII), Part II, Lecture Notes in Computer Science, vol. 7492, pp. 508-517, 2012. Springer (DOI: 10.1007/978-3-642-32964-7_51 and Source Code in C++).
  89. Hui Cheng and Shengxiang Yang. Hyper-mutation based genetic algorithms for dynamic multicast routing problem in mobile ad hoc networks. Proceedings of the 11th IEEE International Conference on Trust, Security, and Privacy in Computing and Communications, pp. 1586-1592, 2012. IEEE Press (DOI: 10.1109/TrustCom.2012.179).
  90. Changhe Li, Shengxiang Yang, and Ming Yang. Maintaining diversity by clustering in dynamic environments. Proceedings of the 2012 IEEE Congress on Evolutionary Computation, 2012. IEEE Press (DOI: 10.1109/CEC.2012.6252880).
  91. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem. Proceedings of the 2012 IEEE Congress on Evolutionary Computation, 2012. IEEE Press (DOI: 10.1109/CEC.2012.6252885).
  92. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization with immigrants schemes for the dynamic vehicle routing problem. EvoApplications 2012: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 7248, pp. 519-528, 2012. Springer (DOI: 10.1007/978-3-642-29178-4_52).
  93. Andreea Vescan, Crina Grosan and Shengxiang Yang. A hybrid evolutionary multiobjective approach for the dynamic component selection problem. Proceedings of the 11th International Conference on Hybrid Intelligent Systems, pp. 714-721, 2011. IEEE Press (DOI: 10.1109/HIS.2011.6122196).
  94. Michalis Mavrovouniotis and Shengxiang Yang. An immigrants scheme based on environmental information for ant colony optimization for the dynamic travelling salesman problem. Proceedings of the 10th International Conference on Artificial Evolution, Lecture Notes in Computer Science, vol. 7401, pp. 1-12, 2011. Springer (DOI: 10.1007/978-3-642-35533-2_1).
  95. Michalis Mavrovouniotis and Shengxiang Yang. An ant system with direct communication for the capacitated vehicle routing problem. Proceedings of the 2011 UK Workshop on Computational Intelligence, pp. 14-19, 2011.
  96. Michalis Mavrovouniotis and Shengxiang Yang. Memory-based immigrants for ant colony optimization in changing environments. EvoApplications 2011: Applications of Evolutionary Computing, Part I, Lecture Notes in Computer Science, vol. 6624, pp. 324-333, 2011. Springer (DOI: 10.1007/978-3-642-20525-5_33).
  97. Sadaf N. Jat and Shengxiang Yang. A guided search non-dominated sorting genetic algorithm for the multi-objective university course timetabling problem. Proceedings of the 11th European Conference on Evolutionary Computation in Combinatorial Optimisation, Lecture Notes in Computer Science, vol. 6622, pp. 1-13, 2011. Springer (DOI: 10.1007/978-3-642-20364-0_1).
  98. Hui Cheng and Shengxiang Yang. Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks. Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, pp. 1-7, 2011. IEEE Press (DOI: 10.1109/CIDUE.2011.5948486 and PDF File).
  99. Renato Tinos and Shengxiang Yang. Evolution strategies with q-Gaussian mutation for dynamic optimization problems. Proceedings of the 11th Brazilian Symposium on Artificial Neural Network, pp. 223-228, 2010. IEEE Press (DOI: 10.1109/SBRN.2010.46 and PDF File).
  100. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization with direct communication for the traveling salesman problem. Proceedings of the 2010 UK Workshop on Computational Intelligence, 2010. IEEE Press. (DOI: 10.1109/UKCI.2010.5625608 and PDF File).
  101. Renato Tinos and Shengxiang Yang. An analysis of the XOR dynamic problem generator based on the dynamical system. Proceedings of the 11th International Conference on Parallel Problems Solving from Nature (PPSN XI), Part I, Lecture Notes in Computer Science, vol. 6238, pp. 274-283. 2010. Springer (DOI: 10.1007/978-3-642-15844-5_28 and PDF File).
  102. Michalis Mavrovouniotis and Shengxiang Yang. Ant colony optimization with immigrants schemes in dynamic environments. Proceedings of the 11th International Conference on Parallel Problems Solving from Nature (PPSN XI), Part II, Lecture Notes in Computer Science, vol. 6239, pp. 371-380. 2010. Springer, (DOI: 10.1007/978-3-642-15871-1_38 and PDF File).
  103. Changhe Li and Shengxiang Yang. Adaptive learning particle swarm optimizer--II for global optimization. Proceedings of the 2010 IEEE Congress on Evolutionary Computation, pp. 779-786, 2010. IEEE Press (DOI: 10.1109/CEC.2010.5586230 and PDF File).
  104. Shakeel Arshad and Shengxiang Yang. A hybrid genetic algorithm and inver over approach for the travelling salesman problem. Proceedings of the 2010 IEEE Congress on Evolutionary Computation, pp. 252-259, 2010. IEEE Press (DOI: 10.1109/CEC.2010.5586216 and PDF File).
  105. Imitiaz Korejo, Shengxiang Yang, and Changhe Li. A directed mutation operator for real coded genetic algorithms. EvoApplications 2010: Applications of Evolutionary Computing, Part I, Lecture Notes in Computer Science, vol. 6024, pp. 491-500, 2010. Springer (DOI: 10.1007/978-3-642-12239-2_51 and PDF File).
  106. Hui Cheng and Shengxiang Yang. Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. EvoApplications 2010: Applications of Evolutionary Computing, Part I, Lecture Notes in Computer Science, vol. 6024, pp. 562-571, 2010. Springer (DOI: 10.1007/978-3-642-12239-2_58 and PDF File).
  107. Imitiaz Korejo, Shengxiang Yang, and Changhe Li. A comparative study of adaptive mutation operators for metaheuristics. Proceedings of the 8th Metaheuristic International Conference, 2009.
  108. Sadaf N. Jat and Shengxiang Yang. A guided search genetic algorithm for the university course timetabling problem. Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory and Applications (MISTA 2009), pp. 180-191, 2009 (PDF File).
  109. Shakeel Arshad, Shengxiang Yang, and Changhe Li. A sequence based genetic algorithm with local search for the travelling salesman problem. Proceedings of the 2009 UK Workshop on Computational Intelligence, pp. 98-105, 2009.
  110. Hui Cheng and Shengxiang Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using tabu search. Proceedings of the 5th International Conference on Natural Computation, vol. 4, pp. 325-330, 2009. IEEE Press (DOI: 10.1109/ICNC.2009.435).
  111. Changhe Li and Shengxiang Yang. An adaptive learning particle swarm optimizer for function optimization. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 381-388, 2009. IEEE Press (DOI: 10.1109/CEC.2009.4982972 and PDF File).
  112. Changhe Li and Shengxiang Yang. A clustering particle swarm optimizer for dynamic optimization. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 439-446, 2009. IEEE Press (DOI: 10.1109/CEC.2009.4982979 and PDF File).
  113. Shengxiang Yang and Hendrik Richter. Hyper-learning for population-based incremental learning in dynamic environments. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 682-689, 2009. IEEE Press (DOI: 10.1109/CEC.2009.4983011 and PDF File).
  114. Hui Cheng and Shengxiang Yang. Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks. Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 3135-3140, 2009. IEEE Press (DOI: 10.1109/CEC.2009.4983340 and PDF File).
  115. Lili Liu, Dingwei Wang, and Shengxiang 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).
  116. Changhe Li and Shengxiang 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).
  117. Hui Cheng and Shengxiang 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).
  118. Changhe Li and Shengxiang 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).
  119. Sadaf N. Jat and Shengxiang 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).
  120. Hui Cheng, Xingwei Wang, Min Huang, and Shengxiang 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).
  121. Hendrik Richter and Shengxiang 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).
  122. Changhe Li and Shengxiang 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).
  123. Chunlin Ji, Yangyang Zhang, Mengmeng Tong, and Shengxiang 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).
  124. Hui Cheng and Shengxiang 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).
  125. Changhe Li, Shengxiang Yang, and Imitiaz Korejo. An adaptive mutation operator for particle swarm optimization. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 165-170, 2008 (PDF File).
  126. Renato Tinos and Shengxiang 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).
  127. Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang Yang, and Dazhi 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).
  128. Shengxiang Yang and Renato 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).
  129. Hendrik Richter and Shengxiang 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).
  130. Lili Liu, Dingwei Wang, and Shengxiang 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).
  131. Renato Tinos and Shengxiang 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).
  132. Renato Tinos and Shengxiang 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).
  133. Shengxiang 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).
  134. Shengxiang 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).
  135. Hongfeng Wang, Dingwei Wang, and Shengxiang 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).
  136. Shengxiang 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).
  137. Shengxiang 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).
  138. Shengxiang 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).
  139. Shengxiang 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).
  140. Shengxiang 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).
  141. Shengxiang Yang and Sima 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).
  142. Shengxiang 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).
  143. Shengxiang 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).
  144. Renato Tinos and Shengxiang 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).
  145. Shengxiang 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).
  146. Shengxiang 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.
  147. Shengxiang 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).
  148. Shengxiang 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).
  149. Shengxiang Yang and Xin 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).
  150. Shengxiang 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).
  151. Shengxiang 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).
  152. Shengxiang 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).
  153. Shengxiang 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).
  154. Shengxiang 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).
  155. Shengxiang 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).
  156. Tomasz Radzik and Shengxiang Yang. Experimantal evaluation of algorithmic solutions for generalized network flow models. Presented in the 17th International Symposium on Mathematical Programming (ISMP'00), August 2000.
  157. Shengxiang Yang and Dingwei 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).
  158. Kai Zhao, Shengxiang Yang and Dingwei 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. Shengxiang 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. Shengxiang 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. Shengxiang Yang and Juergen 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. Shengxiang Yang and Juergen Branke (editors), Proceedings of the 4th Workshop on Evolutionary Algorithms for Dynamic Optimization Problem, 2005.

PhD Thesis

  1. Shengxiang 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. Wenjian Luo, Peilan Xu, Shengxiang Yang, and Yuhui Shi. Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization. Technical Report, arXiv preprint arXiv:2402.02033, February 2024 (PDF File).
  2. Yinan Guo, Guoyu Chen, Caitong Yue, Jing Liang, Yong Wang, and Shengxiang Yang. Benchmark Problems for IEEE WCCI2024 Competition on Dynamic Constrained Multiobjective Optimization. Technical Report, School of Computer Science and Informatics, De Montfort University, U.K., December 2023 (PDF File).
  3. Danial Yazdani, Michalis Mavrovouniotis, Changhe Li, Wenjian Luo, Mohammad Nabi Omidvar, Amir H. Gandomi, Trung Thanh Nguyen, Juergen Branke, Xiaodong Li, Shengxiang Yang, and Xin Yao. Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB). Technical Report, arXiv preprint arXiv:2106.06174v3, December 2023 (PDF File).
  4. Yinan Guo, Guoyu Chen, Caitong Yue, Jing Liang, Yong Wang, and Shengxiang Yang. Benchmark Problems for CEC2023 Competition on Dynamic Constrained Multiobjective Optimization. Technical Report, School of Computer Science and Informatics, De Montfort University, U.K., December 2022 (PDF File).
  5. Wenjian Luo, Xin Lin, Changhe Li, Shengxiang Yang, and Yuhui Shi. Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments. Technical Report, arXiv preprint arXiv:2201.00523v2, January 2022 (PDF File).
  6. Danial Yazdani, Juergen Branke, Mohammad Nabi Omidvar, Xiaodong Li, Changhe Li, Michalis Mavrovouniotis, Trung Thanh Nguyen, Shengxiang Yang, and Xin Yao. IEEE CEC 2022 Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark. Technical Report, arXiv preprint arXiv:2106.06174v2, June 2021 (PDF File).
  7. Hui Yuan, Raouf Hamzaoui, Ferrante Neri, and Shengxiang Yang. Proposal to the MPEG 3DG Standardization Committee. Deliverable D5.2 of the Optimized Dynamic Point Cloud Compression (OPT-PCC) project. Technical Report, De Montfort University, U.K., November 2021 (DORA URI).
  8. Hui Yuan, Raouf Hamzaoui, Ferrante Neri, and Shengxiang Yang. Report on the bit allocation solution. Deliverable D3 of the Optimized Dynamic Point Cloud Compression (OPT-PCC) project. Technical Report, De Montfort University, U.K., August 2021 (DORA URI).
  9. Shouyong Jiang, Shengxiang Yang, Xin 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).
  10. Ran Cheng, Miqing Li, Ye Tian, Xingyi Zhang, Shengxiang Yang, Yaochu Jin and Xin 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).
  11. Ran Cheng, Miqing Li, Ye Tian, Xingyi Zhang, Shengxiang Yang, Yaochu Jin and Xin 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).
  12. Changhe Li, Michalis Mavrovouniotis, Shengxiang Yang, and Xin 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).
  13. Michalis Mavrovouniotis, Changhe Li, Shengxiang Yang, and Xin 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).
  14. Changhe Li, Shengxiang Yang, and David 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).
  15. Changhe Li, Shengxiang Yang, Trung Thanh Nguyen, E. L. Yu, Xin Yao, Yaochu 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).
  16. Shengxiang 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).
  17. Tomasz Radzik and Shengxiang 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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