Shengxiang Yang    BSc, MSc, PhD, SMIEEE

Welcome to Shengxiang Yang's Publications Page

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

Edited Books, Proceedings and Journal Special Issues

  1. S. Yang and X. Yao (eds.), Evolutionary Computation for Dynamic Optimization Problems, in the series Studies in Computational Intelligence, vol. 490. Springer, Heidelberg, ISSN: 1860-949X (Print) 1860-9503 (Online), ISBN: 978-3-642-38415-8 (Print) 978-3-642-38416-5 (eBook), May 2013 (Product Flyer, Front Matter, and DOI: 10.1007/978-3-642-38416-5). The book was one of the top 25% most downloaded eBooks in the relevant Springer eBook Collection in 2013.
  2. S. Yang, Y.-S. Ong, and Y. Jin (eds.), Evolutionary Computation in Dynamic and Uncertain Environments, in the series Studies in Computational Intelligence, vol. 51. Springer, Heidelberg, ISSN: 1860-949X (Print) 1860-9503 (Online), ISBN: 978-3-540-49772-1 (Print) 978-3-540-49774-5 (Online), March 2007 (Front Matter and DOI: 10.1007/978-3-540-49774-5).
  3. Jin, Y.; Polikar, R; Yang, S. (eds.), Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4799-4516-0, December 2014. (DOI: 10.1109/CIDUE.2014.7007856).
  4. Jin, Y.; Polikar, R; Yang, S. (eds.), Proceedings of the 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4673-5849-1, April 2013 (DOI: 10.1109/CIDUE.2013.6595764).
  5. Jin, Y.; Yang, S.; Polikar, R. (eds.), Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, IEEE Press, ISBN: 978-1-4244-9930-4, April 2011 (DOI: 10.1109/CIDUE.2011.5948495).
  6. 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).
  7. 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).
  8. 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).
  9. 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).
  10. F. Neri and S. Yang (guest editors), Memetic Computing in the Presence of Uncertainties, Thematic Issue of Memetic Computing, Vol. 2, No. 2, pp. 85-162, June 2010, Springer, ISSN: 1865-9284 (Print) 1865-9292 (Online).
  11. S. Yang, Y.-S. Ong, and Y. Jin (guest editors), Evolutionary Computation in Dynamic and Uncertain Environments, Special Issue of Genetic Programming and Evolvable Machines, Vol. 7, No. 4, pp. 293-404, December 2006, Springer Netherlands, ISSN: 1389-2576 (Print) 1573-7632 (Online).

Refereed Journal Papers (and Source Codes)

  1. J. Zou, Y. Zhang, and S. Yang. Adaptive neighborhood selection for many-objective optimization problems. Applied Soft Computing, accepted in October, 2017. Elsevier.
  2. Y. Wang, H. Liu, H. Long, Z. Zhang, and S. Yang. Differential evolution with a new encoding mechanism for optimizing wind farm layout. IEEE Transactions on Industrial Informatics, published online first: 22 September 2017. IEEE Press (DOI: 10.1109/TII.2017.2743761).
  3. K. Wang, X. Li, C. Jia, S. Yang, M. Li, and Y. Li. Multiobjective optimization of the production process for ground granulated blast furnace slags. Soft Computing, published online first: 04 August 2017. Springer (DOI: 10.1007/s00500-017-2761-x).
  4. S. Jiang, S. Yang, Y. Wang, and X. Liu. Scalarizing functions in decomposition-based multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, published online first: 29 June 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2707980).
  5. M. Li, C. Grosan, S. Yang, X. Liu, and X. Yao. Multi-line distance minimization: A visualized many-objective test problem suite. IEEE Transactions on Evolutionary Computation, published online first: 18 January 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2655451 and Source Code in C).
  6. J. Zou, Q. Li, S. Yang, H. Bai, and J. Zheng. A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization. Applied Soft Computing, 61: 806-818, December 2017. Elsevier (DOI: 10.1016/j.asoc.2017.08.004).
  7. J. Eaton, S. Yang, and M. Gongora. Ant colony optimization for simulated dynamic multi-objective railway junction rescheduling. IEEE Transactions on Intelligent Transportation Systems, 18(11): 2980-2992, November 2017. IEEE Press (DOI: 10.1109/TITS.2017.2665042).
  8. W. Gong, Y. Wang, Z. Cai, and S. Yang. A weighted biobjective transformation technique for locating multiple optimal solutions of nonlinear equation systems. IEEE Transactions on Evolutionary Computation, 21(5): 697-713, October 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2670779 and Source Code in C++).
  9. Y. Wang, B. Xu, G. Sun, and S. Yang. A two-phase differential evolution for uniform designs in constrained experimental domains. IEEE Transactions on Evolutionary Computation, 21(5): 665-680, October 2017. IEEE Press (DOI: 10.1109/TEVC.2017.2669098 and Source Code in Matlab).
  10. X. Wang, J. Zhang, M. Huang, and S. Yang. A green intelligent routing algorithm supporting flexible QoS for many-to-many multicast. Computer Networks, 126: 229-245, October 2017. Elsevier (DOI: 10.1016/j.comnet.2017.07.010).
  11. G. Ruan, G. Yu, J. Zheng, J. Zou, and S. Yang. The effect of diversity maintenance on prediction in dynamic multi-objective optimization. Applied Soft Computing, 58: 631-647, September 2017. Elsevier (DOI: 10.1016/j.asoc.2017.05.008).
  12. S. Yang, S. Jiang, and Y. Jiang. Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes. Soft Computing, 21(16): 4677-4691, August 2017. Springer (DOI: 10.1007/s00500-016-2076-3).
  13. M. Mavrovouniotis, F. M. Muller, and S. Yang. Ant colony optimization with local search for dynamic travelling salesman problems. IEEE Transactions on Cybernetics, 47(7): 1743-1756, July 2017. IEEE Press (DOI: 10.1109/TCYB.2016.2556742 and Source Code in C++).
  14. S. Jiang and S. Yang. A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary Computation, 21(3): 329-346, June 2017. IEEE Press (DOI: 10.1109/TEVC.2016.2592479 and Source Code in C).
  15. M. T. Younis and S. Yang. A genetic algorithm for independent job scheduling in grid computing. MENDEL - Soft Computing Journal, 23(1): 65-72, June 2017. Brno University of Technology, Brno, Czech Republic.
  16. M. Mavrovouniotis, C. Li, and S. Yang. A survey of swarm intelligence for dynamic optimization: Algorithms and applications. Swarm and Evolutionary Computation, 33: 1-17, April 2017. Elsevier (DOI: 10.1016/j.swevo.2016.12.005).
  17. R. Cheng, M. Li, Y. Tian, X. Zhang, S. Yang, Y. Jin, and X. Yao. A benchmark test suite for evolutionary many-objective optimization. Complex and Intelligent Systems, 3(1): 67-81, March 2017. Springer (DOI: 10.1007/s40747-017-0039-7 and Source Code in Matlab).
  18. S. Jiang and S. Yang. A steady-state and generational evolutionary algorithm for dynamic multi-objective optimization. IEEE Transactions on Evolutionary Computation, 21(1): 65-82, February 2017. IEEE Press (DOI: 10.1109/TEVC.2016.2574621 and Source Code in C).
  19. S. Jiang and S. Yang. Evolutionary dynamic multi-objective optimization: benchmarks and algorithm comparisons. IEEE Transactions on Cybernetics, 47(1): 198-211, January 2017. IEEE Press (DOI: 10.1109/TCYB.2015.2510698 and Source Code in C).
  20. Z. Li, J. Guo, and S. Yang. Improving the JADE algorithm by clustering successful parameters. International Journal of Wireless and Mobile Computing, 11(3): 190-197, December 2016. Inderscience Publishers Ltd (DOI: 10.1504/IJWMC.2016.081159).
  21. M. Li, S. Yang, and X. Liu. Pareto or non-pareto: Bi-criterion evolution in multi-objective optimization. IEEE Transactions on Evolutionary Computation, 20(5): 645-665, October 2016. IEEE Press (DOI: 10.1109/TEVC.2015.2504730 and Source Code in C).
  22. Y. Zhang, M. Peng, and S. Yang. A clique-based online algorithm for constructing optical orthogonal codes. Applied Soft Computing, 47: 21-32, October 2016. Elsevier (DOI: 10.1016/j.asoc.2016.05.024).
  23. C. Li, T. T. Nguyen, M. Yang, M. Mavrovouniotis, and S. Yang. An adaptive multi-population framework for locating and tracking multiple optima. IEEE Transactions on Evolutionary Computation, 20(4): 590-605, August 2016. IEEE Press (DOI: 10.1109/TEVC.2015.2504383).
  24. J. Eaton, S. Yang, and M. Mavrovouniotis. Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays. Soft Computing, 20(8): 2951-2966, August 2016. Springer (DOI: 10.1007/s00500-015-1924-x).
  25. S. Jiang and S. Yang. An improved multi-objective optimization evolutionary algorithm based on decomposition for complex Pareto fronts. IEEE Transactions on Cybernetics, 46(2): 421-437, February 2016. IEEE Press (DOI: 10.1109/TCYB.2015.2403131 and Source Code in C).
  26. W. Fang, S. Yang, and X. Yao. A survey on problem models and solution approaches to rescheduling in railway networks. IEEE Transactions on Intelligent Transportation Systems, 16(6): 2997-3016, December 2015. IEEE Press (DOI: 10.1109/TITS.2015.2446985).
  27. A. Akutekwe, H. Seker, and S. Yang. In Silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimization approach. IET Systems Biology, 9(6): 294-302, December 2015. The Institution of Engineering and Technology (IET) (DOI: 10.1049/iet-syb.2015.0031).
  28. M. Li, S. Yang, and X. Liu. Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228: 45-65, November, 2015. Elsevier (DOI: 10.1016/j.artint.2015.06.007).
  29. M. Mavrovouniotis and S. Yang. Training neural networks with ant colony optimization algorithms for pattern classification. Soft Computing, 19(6): 1511-1522, June, 2015. Springer (DOI: 10.1007/s00500-014-1334-5).
  30. C. Li, T. T. Nguyen, M. Yang, S. Yang, and S. Zeng. Multi-population methods in unconstrained continuous dynamic environments: the challenges. Information Sciences, 296: 95-118, March 2015. Elsevier (DOI: 10.1016/j.ins.2014.10.062).
  31. M. Mavrovouniotis and S. Yang. Ant algorithms with immigrants schemes for the dynamic vehicle routing problem. Information Sciences, 294: 456-477, February 2015. Elsevier (DOI: 10.1016/j.ins.2014.10.002 and Source Code in C++).
  32. M. Li, S. Yang, and X. Liu. Diversity comparison of Pareto front approximations in many-objective optimization. IEEE Transactions on Cybernetics, 44(12): 2568-2584, December 2014. IEEE Press (DOI: 10.1109/TCYB.2014.2310651 and Source Code in C).
  33. C. Li, S. Yang, and M. Yang. An adaptive multi-swarm optimizer for dynamic optimization problems. Evolutionary Computation, 22(4): 559-594, Winter 2014. The MIT Press (DOI: 10.1162/EVCO_a_00117).
  34. R. Tinos and S. Yang. Analysis of fitness landscape modifications in evolutionary dynamic optimization. Information Sciences, 282: 214-236, October 2014. Elsevier (DOI: 10.1016/j.ins.2014.05.053 and Source Code in C++ for the implemented DOP benchmark generator).
  35. M. Li, S. Yang, K. Li, and X. Liu. Evolutionary algorithms with segment-based search for multiobjective optimization problems. IEEE Transactions on Cybernetics, 44(8): 1295-1313, August 2014. IEEE Press (DOI: 10.1109/TCYB.2013.2282503).
  36. W. Kong, T. Chai, J. Ding, and S. Yang. Multifurnace optimization in electric smelting plants by load scheduling and control. IEEE Transactions on Automation Science and Engineering, 11(3): 850-862, July 2014. IEEE Press (DOI: 10.1109/TASE.2014.2309348).
  37. M. Li, S. Yang, and X. Liu. Shift-based density estimation for Pareto-based algorithms in many-objective optimization. IEEE Transactions on Evolutionary Computation, 18(3): 348-365, June 2014. IEEE Press (DOI: 10.1109/TEVC.2013.2262178 and Source Code in C).
  38. M. Li, S. Yang, J. Zheng, and X. Liu. ETEA: A Euclidean minimum spanning tree-based evolutionary algorithm for multiobjective optimization. Evolutionary Computation, 22(2): 189-230, Summer 2014. The MIT Press (DOI: 10.1162/EVCO_a_00106 and Source Code in C).
  39. S. Yang, M. Li, X. Liu, and J. Zheng. A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, 17(5): 721-736, October 2013. IEEE Press (DOI: 10.1109/TEVC.2012.2227145 and Source Code in C).
  40. M. Mavrovouniotis and S. Yang. Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors. Applied Soft Computing, 13(10): 4023-4037, October 2013. Elsevier (DOI: 10.1016/j.asoc.2013.05.022 and Source Code in C++).
  41. S. Yang, Y. Jiang, and T. T. Nguyen. Metaheuristics for dynamic combinatorial optimization problems. IMA Journal of Management Mathematics, 24(4): 451-480, October 2013. Oxford University Press (Invited survey paper, DOI: 10.1093/imaman/DPS021).
  42. Y. Cui, M. Huang, S. Yang, L. H. Lee, and X. Wang. Fourth party logistics routing problem model with fuzzy duration time and cost discount. Knowledge-Based Systems, 50: 14-24, September, 2013. Elsevier (DOI: 10.1016/j.knosys.2013.04.020).
  43. M. Huang, Y. Cui, S. Yang, and X. Wang. Fourth party logistics routing problem with fuzzy duration time. International Journal of Production Economics, 145(1): 107-116, September 2013. Elsevier (DOI: 10.1016/j.ijpe.2013.03.007).
  44. I. Korejo, S. Yang, K. Brohi, and Z.U.A. Khuhro. Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization Problems. International Journal on Soft Computing, Artificial Intelligence and Application, 2(2): 1-19, April 2013. Academy and Industry Research Collaboration Center (AIRCC) (DOI: 10.5121/ijscai.2013.2201).
  45. W. Kong, T. Chai, S. Yang, and J. Ding. A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing. Applied Soft Computing, 13(5): 2960-2969, March 2013. Elsevier (DOI: 10.1016/j.asoc.2012.02.025).
  46. H. Cheng, S. Yang, and J. Cao. Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks. Expert Systems with Applications, 40(4): 1381-1392, March 2013. Elsevier (DOI: 10.1016/j.eswa.2012.08.050).
  47. T. T. Nguyen, S. Yang, and J. Branke. Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation, 6: 1-24, October 2012. Elsevier (Invited survey paper, DOI: 10.1016/j.swevo.2012.05.001).
  48. H. Cheng, S. Yang, and X. Wang. Immigrants enhanced multi-population genetic algorithms for dynamic shortest path routing problems in mobile ad hoc networks. Applied Artificial Intelligence, 26(7): 673-695, August 2012. Taylor & Francis (DOI: 10.1080/08839514.2012.701449).
  49. C. Li and S. Yang. A general framework of multi-population methods with clustering in undetectable dynamic environments. IEEE Transactions on Evolutionary Computation, 16(4): 556-577, August 2012. IEEE Press (DOI: 10.1109/TEVC.2011.2169966, PDF File, and Source Code in C++ with details on EAlib available here).
  50. H. Wang, I.-K. Moon, S. Yang, and D. Wang. A memetic particle swarm optimization algorithm for multimodal optimization problems. Information Sciences, 197: 38-52, August 2012. Elsevier (DOI: 10.1016/j.ins.2012.02.016).
  51. H. Wang, S. Yang, W. H. Ip, and D. Wang. A memetic particle swarm optimization algorithm for dynamic multi-modal optimization problems. International Journal of Systems Science, 43(7): 1268-1283, July 2012. Taylor & Francis (DOI: 10.1080/00207721.2011.605966 and PDF File).
  52. C. Li, S. Yang, and T. T. Nguyen. A self-learning particle swarm optimizer for global optimization problems. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 42(3): 627-646, June 2012. IEEE Press (DOI: 10.1109/TSMCB.2011.2171946 and PDF File).
  53. L. Liu, S. Yang, and D. Wang. Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima. Information Sciences, 182(1): 139-155, January 2012. Elsevier (DOI: 10.1016/j.ins.2010.11.013 and PDF File).
  54. S. N. Jat and S. Yang. A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling. Journal of Scheduling, 14(6): 617-637, December 2011. Springer (DOI: 10.1007/s10951-010-0202-0 and PDF File).
  55. R. Tinos and S. Yang. Use of the q-Gaussian mutation in evolutionary algorithms. Soft Computing, 15(8): 1523-1549, August 2011. Springer (DOI: 10.1007/s00500-010-0686-8, PDF File and Source Code in C++).
  56. M. Mavrovouniotis and S. Yang. A memetic ant colony optimization algorithm for the dynamic traveling salesman problem. Soft Computing, 15(7): 1405-1425, July 2011. Springer (DOI: 10.1007/s00500-010-0680-1 and PDF File).
  57. R. Tinos and S. Yang. Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems. International Journal of Hybrid Intelligent Systems, 8(3): 155-168, June 2011. IOS Press (DOI: 10.3233/HIS-2011-0136 and PDF File).
  58. I. Comsa, C. Grosan, and S. Yang. A brief analysis of evolutionary algorithms for the dynamic multiobjective subset sum problem. Studia Univ. Babes-Bolyai, Informatica, LVI(2): 88-94, June 2011. Babes-Bolyai University, Romania (PDF File).
  59. H. Cheng and S. Yang. Joint QoS multicast routing and channel assignment in multiradio multichannel wireless mesh networks using intelligent computational methods. Applied Soft Computing, 11(2): 1953-1964, March 2011. Elsevier (DOI: 10.1016/j.asoc.2010.06.011, PDF File, and Source Code in C++).
  60. X. Peng, X. Gao, and S. Yang. Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Computing, 15(2): 311-326, February 2011. Springer (DOI: 10.1007/s00500-010-0547-5, PDF File, and Source Code in Microsoft Visual C++ 6.0).
  61. S. Yang and S. N. Jat. Genetic algorithms with guided and local search strategies for university course timetabling. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 41(1): 93-106, January 2011. IEEE Press (DOI: 10.1109/TSMCC.2010.2049200 and PDF File).
  62. S. Yang and C. Li. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation, 14(6): 959-974, December 2010. IEEE Press (DOI: 10.1109/TEVC.2010.2046667, PDF File, and Source Code in GNU C++).
  63. L. Liu, S. Yang, and D. Wang. Particle swarm optimization with composite particles in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 40(6): 1634-1648, December 2010. IEEE Press (DOI: 10.1109/TSMCB.2010.2043527 and PDF File).
  64. H. Wang, S. Yang, W. H. Ip, and D. Wang. A particle swarm optimization based memetic algorithm for dynamic optimization problems. Natural Computing, 9(3): 703-725, September 2010. Springer (DOI: 10.1007/s11047-009-9176-2 and PDF File).
  65. H. Cheng and S. Yang. Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Engineering Applications of Artificial Intelligence, 23(5): 806-819, August 2010. Elsevier (DOI: 10.1016/j.engappai.2010.01.021, PDF File, C++ Source Code for the General Dynamics Model, and C++ Source Code for the Worst Dynamics Model).
  66. H. Cheng, X. Wang, S. Yang, M. Huang, and J. Cao. QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms. Journal of Network and Computer Applications, 33(4): 512-522, July 2010. Elsevier (DOI: 10.1016/j.jnca.2010.01.001 and PDF File).
  67. S. Yang, D. Wang, T. Chai, and G. Kendall. An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1): 17-38, February 2010. Springer (DOI: 10.1007/s10951-009-0106-z and PDF File).
  68. S. Yang, H. Cheng, and F. Wang. Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 40(1): 52-63, January 2010. IEEE Press (DOI: 10.1109/TSMCC.2009.2023676, PDF File, and Source Code in C++).
  69. H. Wang, S. Yang, W. H. Ip, and D. Wang. Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6): 1348-1361, December 2009. IEEE Press (DOI: 10.1109/TSMCB.2009.2015281, and PDF File).
  70. H. Richter and S. Yang. Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Computing, 13(12): 1163-1173, October 2009. Springer (DOI: 10.1007/s00500-009-0420-6 and PDF File).
  71. H. Wang, D. Wang, and S. Yang. A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems. Soft Computing, 13(8-9): 763-780, July 2009. Springer (DOI: 10.1007/s00500-008-0347-3, PDF File, and Source Code in Java).
  72. H. Cheng, J. Cao, X. Wang, S. K. Das, and S. Yang. Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility. Wireless Communications and Mobile Computing, 9(6): 759-771, June 2009. John Wiley & Sons, Ltd (DOI: 10.1002/wcm.627 and PDF File).
  73. H. Cheng, X. Wang, S. Yang, and M. Huang. A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks. Applied Soft Computing, 9(2): 677-684, March 2009. Elsevier (DOI: 10.1016/j.asoc.2008.09.008 and PDF File).
  74. S. Yang and X. Yao. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5): 542-561, October 2008. IEEE Press (DOI: 10.1109/TEVC.2007.913070, PDF File, and Source Code in GNU C++).
  75. S. Yang. Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evolutionary Computation, 16(3): 385-416, Fall 2008. The MIT Press (DOI: 10.1162/evco.2008.16.3.385, PDF File, and Source Code in GNU C++).
  76. R. Tinos and S. Yang. A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines, 8(3): 255-286, September 2007. Springer (DOI: 10.1007/s10710-007-9024-z and PDF File).
  77. S. Yang and R. Tinos. A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing, 4(3): 243-254, July 2007. Springer (DOI: 10.1007/s11633-007-0243-9, PDF File, and Source Code in GNU C++).
  78. H. Wang, D. Wang, and S. Yang. Evolutionary algorithms in dynamic environments. Control and Decision, 22(2): 127-131+137, February 2007.
  79. S. Yang and X. Yao. Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9(11): 815-834, November 2005. Springer (DOI: 10.1007/s00500-004-0422-3 and PDF File).
  80. S. Yang. Adaptive group mutation for tackling deception in genetic search. WSEAS Transactions on Systems, 3(1): 107-112, January 2004 (PDF File).
  81. S. Yang and D. Wang. A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Computers and Operations Research, 28(10): 955-971, September 2001. Elsevier Science Ltd (DOI: 10.1016/S0305-0548(00)00018-6 and PDF File).
  82. S. Yang and D. Wang. Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling. IEEE Transactions on Neural Networks, 11(2): 474-486, March 2000. IEEE Press (DOI: 10.1109/72.839016 and PDF File).
  83. S. Yang and D. Wang. A neural network and heuristics hybrid strategy for job-shop scheduling problem. Journal of Systems Engineering, 14(2): 140-144, June 1999 (PDF File).
  84. S. Yang and D. Wang. Using constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. Information and Control, 28(2): 121-126, April 1999 (PDF File).
  85. S. Yang and D. Wang. Genetic algorithm and adaptive neural network hybrid method for job-shop scheduling problems. Control and Decision, 13(Suppl.): 402-407, July 1998 (PDF File).
  86. S. Yang and D. Wang. Solving optimization and scheduling problems with neural network methods. Systems Engineering, 15(Suppl.): 66-71, December 1997 (PDF File).

Non-Refereed Journal Papers

  1. F. Neri and S. Yang. Guest editorial: Memetic computing in the presence of uncertainties. Memetic Computing, 2(2): 85-86, June 2010. Springer (DOI: 10.1007/s12293-010-0033-8 and PDF File).
  2. S. Yang, Y.-S. Ong, and Y. Jin. Editorial to special issue on evolutionary computation in dynamic and uncertain environments. Genetic Programming and Evolvable Machines, 7(4): 293-294, December 2006. Springer (DOI: 10.1007/s10710-006-9016-4 and PDF File).

Invited / Contributed Book Chapters

  1. M. Mavrovouniotis and S. Yang. Ant colony optimization for dynamic combinatorial optimization problems. In Y. Tan (Ed.), Swarm Intelligence - From Concepts to Applications, Chapter 7, pp. xxx-xxx, 2017. Print: 978-1-78561-313-5, eBook: 978-1-78561-314-2, IET Book, UK.
  2. C. Fahy and S. Yang. Dynamic stream clustering using ants. In P. Angelov, A. Gegov, C. Jayne, and Q. Shen (Eds.), Advances in Computational Intelligence Systems, Volume 513 of the series Advances in Intelligent Systems and Computing, Chapter 32, pp. 495-508, 2016. Springer (DOI: 10.1007/978-3-319-46562-3_32).
  3. M. Mavrovouniotis and S. Yang. Dynamic vehicle routing: A memetic ant colony optimization approach. In A. S. Uyar, E. Ozcan, and N. Urquhart (Eds.), Automated Scheduling and Planning, Volume 505 of the series Studies in Computational Intelligence, Chapter 11, pp. 283-301, Springer-Verlag, December 2013 (DOI: 10.1007/978-3-642-39304-4_11).
  4. S. Yang, T. T. Nguyen, and C. Li. Evolutionary dynamic optimization: test and evaluation environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 1, pp. 3-37, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_1).
  5. T. T. Nguyen, S. Yang, J. Branke, and X. Yao. Evolutionary dynamic optimization: methodologies. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 2, pp. 39-64, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_2).
  6. C. Li and S. Yang. A comparative study on particle swarm optimization in dynamic environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 5, pp. 109-136, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_5).
  7. H. Wang and S. Yang. Memetic algorithms for dynamic optimization problems. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 6, pp. 137-170, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_6).
  8. R. Tinos and S. Yang. Analyzing evolutionary algorithms for dynamic optimization problems based on the dynamical systems approach. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 10, pp. 241-267, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_10).
  9. I. Comsa, C. Grosan, and S. Yang. Dynamics in the multi-objective subset sum: analysing the behaviour of population based algorithms. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 12, pp. 299-313, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_12).
  10. M. Mavrovouniotis and S. Yang. Ant colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problem. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 13, pp. 317-341, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_13).
  11. H. Cheng and S. Yang. Genetic algorithms for dynamic routing problems in mobile ad hoc networks. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 14, pp. 343-375, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_14).
  12. X. Peng, S. Yang, D. Xu, and X. Gao. Evolutionary algorithms for the multiple unmanned aerial combat vehicles anti-ground attack problem in dynamic environments. In S. Yang and X. Yao (Eds.), Evolutionary Computation for Dynamic Optimization Problems, Volume 490 of the series Studies in Computational Intelligence, Chapter 16, pp. 403-431, Springer-Verlag Berlin Heidelberg, May 2013 (DOI: 10.1007/978-3-642-38416-5_16).
  13. H. Richter and S. Yang. Dynamic optimization using analytic and evolutionary approaches: A comparative review. In I. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, Chapter 1, pp. 1-28, Springer-Verlag Berlin Heidelberg, 2013 (DOI: 10.1007/978-3-642-30504-7_1).
  14. Y. Yan, S. Yang, D. Wang, and D. Wang. Agent based evolutionary dynamic optimization. In R. Sarker and T. Ray (eds.), Agent Based Evolutionary Search, Chapter 5, pp. 97-116, Springer-Verlag Berlin Heidelberg, 2010 (DOI: 10.1007/978-3-642-13425-8_5 and PDF File).
  15. H. Cheng, X. Wang, M. Huang, and S. Yang. A review of personal communications services. In K. Y. Chen and H. K. Lee (Eds.), Mobile Computing Research and Applications, Chapter 8, pp. 149-165, Nova Science Publishers, 3rd Quarter, 2009 (ISBN: 978-1-60741-101-7) (PDF File).
  16. S. Yang. Explicit memory schemes for evolutionary algorithms in dynamic environments. In S. Yang, Y.-S. Ong, and Y. Jin (Eds.), Evolutionary Computation in Dynamic and Uncertain Environments, Volume 51 of the series Studies in Computational Intelligence, Chapter 1, pp. 3-28, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_1 and PDF File).
  17. R. Tinos and S. Yang. Genetic algorithms with self-organizing behaviour in dynamic environments. In S. Yang, Y.-S. Ong, and Y. Jin (Eds.), Evolutionary Computation in Dynamic and Uncertain Environments, Volume 51 of the series Studies in Computational Intelligence, Chapter 5, pp. 105-127, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_5 and PDF File).
  18. S. Yang. Adaptive mutation using statistics mechanism for genetic algorithms. In F. Coenen, A. Preece and A. Macintosh (Eds.), Research and Development in Intelligent Systems XX, pp. 19-32, March 2004. London: Springer-Verlag (DOI: 10.1007/978-0-85729-412-8_2 and PDF File).
  19. S. Yang. PDGA: the primal-dual genetic algorithm. In A. Abraham, M. Koppen and K. Franke (Eds.), Design and Application of Hybrid Intelligent Systems, pp. 214-223, 2003. IOS Press (DOI: 10.13140/RG.2.1.3134.3445 and PDF File).
  20. S. Yang. Genetic algorithms based on primal-dual chromosomes for royal road functions. In A. Grmela and N. E. Mastorakis (Eds.), Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 174-179, 2002. WSEAS Press (PDF File).

Refereed Conference Papers (and Source Codes)

  1. D. M. Chitty, S. Yang and M. Gongora. Considering flexibility in the evolutionary dynamic optimisation of airport security lane schedules. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2017. IEEE Press.
  2. M. Mavrovouniotis, M. Van and S. Yang. Pheromone modification strategy for the dynamic travelling salesman problem with weight changes. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2017. IEEE Press.
  3. L. Fu, J. Zou, S. Yang, G. Ruan, J. Zheng and Z. Ma. A proportion-based selection scheme for multi-objective optimization. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, pp. 1-8, 2017. IEEE Press.
  4. C. Fahy, S. Yang and M. 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.
  5. M. Mavrovouniotis, A. Ioannou, and S. 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: 10.1007/978-3-319-55849-3_12).
  6. M. T. Younis, S. Yang and B. 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: 10.1007/978-3-319-55792-2_9).
  7. S. Jiang, S. Yang and M. 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).
  8. D. M. Chitty, M. Gongora and S. 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).
  9. J. Eaton and S. 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).
  10. R. Tinos and S. 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).
  11. S. Jiang and S. 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).
  12. J.-P. Li, Y. Wang, S. Yang and Z. 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).
  13. Z.-Z. Liu, Y. Wang, S. Yang and Z. 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).
  14. S. Jiang, S. Yang and J. 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).
  15. M. Mavrovouniotis and S. 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).
  16. M. Mavrovouniotis and S. 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.
  17. M. Mavrovouniotis and S. 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).
  18. S. Jiang and S. 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.
  19. S. Jiang and S. Yang. Approximating multiobjective optimization problems with complex pareto fronts. Proceedings of the 15th UK Workshop on Computational Intelligence, pp. 1-8, 2015. IEEE Press.
  20. J. Qi, L. Chen, W. Leister and S. 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).
  21. M. Mavrovouniotis, F. M. Muller and S. 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).
  22. M. Li, S. Yang and X. 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).
  23. M. Mavrovouniotis, F. Neri and S. 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).
  24. M. Mavrovouniotis and S. 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).
  25. M. Mavrovouniotis and S. 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).
  26. S. Jiang and S. 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).
  27. M. Mavrovouniotis, S. Yang and X. 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).
  28. J. Eaton and S. 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).
  29. S. Jiang and S. 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).
  30. M. Li, S. Yang, and X. 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.
  31. M. Mavrovouniotis and S. 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).
  32. M. Mavrovouniotis and S. 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).
  33. S. Jiang and S. 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).
  34. R. Hu, S. Yang, and X. 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).
  35. M. Mavrovouniotis and S. 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).
  36. M. Mavrovouniotis and S. 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).
  37. W. Kong, T. Chai, J. Ding, S. Yang, and X. Zheng. 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).
  38. M. Mavrovouniotis and S. 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).
  39. M. Li, S. Yang, and X. Liu, and R. 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).
  40. M. Li, S. Yang, and X. Liu, and K. 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).
  41. Y. Liu, T. Chai, S. J. Qin, Q. Pan, and S. 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).
  42. Z. Wu, S. Yang, and D. 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).
  43. M. Mavrovouniotis, S. Yang, and X. 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++).
  44. H. Cheng and S. 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).
  45. C. Li, S. Yang, and M. 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).
  46. M. Mavrovouniotis and S. 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).
  47. M. Mavrovouniotis and S. 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).
  48. A. Vescan, C. Grosan and S. 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).
  49. M. Mavrovouniotis and S. 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).
  50. M. Mavrovouniotis and S. 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.
  51. M. Mavrovouniotis and S. 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).
  52. S. N. Jat and S. 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).
  53. H. Cheng and S. 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).
  54. R. Tinos and S. 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).
  55. M. Mavrovouniotis and S. 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).
  56. R. Tinos and S. 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).
  57. M. Mavrovouniotis and S. 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).
  58. C. Li and S. 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).
  59. S. Arshad and S. 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).
  60. I. Korejo, S. Yang, and C. 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).
  61. H. Cheng and S. 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).
  62. I. Korejo, S. Yang, and C. Li. A comparative study of adaptive mutation operators for metaheuristics. Proceedings of the 8th Metaheuristic International Conference, 2009.
  63. S. N. Jat and S. 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).
  64. S. Arshad, S. Yang, and C. 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.
  65. H. Cheng and S. 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).
  66. C. Li and S. 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).
  67. C. Li and S. 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).
  68. S. Yang and H. 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).
  69. H. Cheng and S. 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).
  70. L. Liu, D. Wang, and S. Yang. An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems. EvoWorkshops 2009: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 5484, pp. 725-734, 2009. Springer (DOI: 10.1007/978-3-642-01129-0_82 and PDF File).
  71. C. Li and S. Yang. An island based hybrid evolutionary algorithm for optimization. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 180-189, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_19 and PDF File).
  72. H. Cheng and S. Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using simulated annealing. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 370-380, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_38 and PDF File).
  73. C. Li and S. Yang. A generalized approach to construct benchmark problems for dynamic optimization. Proceedings of the 7th International Conference on Simulated Evolution and Learning, Lecture Notes in Computer Science, vol. 5361, pp. 391-400, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_40 and PDF File).
  74. S. N. Jat and S. Yang. A memetic algorithm for the university course timetabling problem. Proceedings of the 20th IEEE International Conference on Tools with Artificial Intelligence, vol. 1, pp. 427-433, 2008. IEEE Press (DOI: 10.1109/ICTAI.2008.126 and PDF File).
  75. H. Cheng, X. Wang, M. Huang, and S. Yang. A review of personal communications services. Proceedings of the 9th International Conference for Young Computer Scientists, pp. 616-621, 2008. IEEE Press (DOI: 10.1109/ICYCS.2008.191).
  76. H. Richter and S. Yang. Learning in abstract memory schemes for dynamic optimization. Proceedings of the 4th International Conference on Natural Computation, vol. 1, pp. 86-91, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.110).
  77. C. Li and S. Yang. Fast multi-swarm optimization for dynamic optimization problems. Proceedings of the 4th International Conference on Natural Computation, vol. 7, pp. 624-628, 2008. IEEE Press (DOI: 10.1109/ICNC.2008.313 and PDF File).
  78. C. Ji, Y. Zhang, M. Tong, and S. Yang. Particle filter with swarm move for optimization. Proceedings of the 10th International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, vol. 5199, pp. 909-918, 2008. Springer (DOI: 10.1007/978-3-540-87700-4_90 and PDF File).
  79. H. Cheng and S. Yang. A genetic-inspired joint multicast routing and channel assignment algorithm in wireless mesh networks. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 159-164, 2008 (PDF File).
  80. C. Li, S. Yang, and I. Korejo. An adaptive mutation operator for particle swarm optimization. Proceedings of the 2008 UK Workshop on Computational Intelligence, pp. 165-170, 2008 (PDF File).
  81. R. Tinos and S. Yang. Evolutionary programming with q-Gaussian mutation for dynamic pptimization problems. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1823-1830, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631036 and PDF File).
  82. Y. Yan, H. Wang, D. Wang, S. Yang, and D. Z. Wang. A multi-agent based evolutionary algorithm in non-stationary environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 2967-2974, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631198 and PDF File).
  83. S. Yang and R. Tinos. Hyper-selection in dynamic environments. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 3185-3192, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631229 and PDF File).
  84. H. Richter and S. Yang. Memory based on abstraction for dynamic fitness functions. In EvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4974, pp. 597-606, 2008. Springer (DOI: 10.1007/978-3-540-78761-7_65 and PDF File).
  85. L. Liu, D. Wang, and S. Yang. Compound particle swarm optimization in dynamic environments. In EvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4974, pp. 616-625, 2008. Springer (DOI: 10.1007/978-3-540-78761-7_67 and PDF File).
  86. R. Tinos and S. Yang. Self-adaptation of mutation distribution in evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 79-86, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424457 and PDF File).
  87. R. Tinos and S. Yang. Continuous dynamic problem generators for evolutionary algorithms. Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 236-243, 2007. IEEE Press (DOI: 10.1109/CEC.2007.4424477 and PDF File).
  88. S. Yang. Learning the dominance in diploid genetic algorithms for changing optimization problems. Proceedings of the 2nd International Symposium on Intelligence Computation and Applications, pp. 157-162, 2007. China University of GeoSciences Press (PDF File).
  89. S. Yang. Genetic algorithms with elitism-based immigrants for chaning 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).
  90. H. Wang, D. Wang, and S. Yang. Triggered memory-based swarm optimization in dynamic environments. EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 4448, pp. 637-646, 2007. Springer (DOI: 10.1007/978-3-540-71805-5_70 and PDF File).
  91. S. Yang. On the design of diploid genetic algorithms for problem optimization in dynamic environments. Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pp. 1362-1369, 2006. IEEE Press (DOI: 10.1109/CEC.2006.1688467 and PDF File).
  92. S. Yang. Job-shop scheduling with an adaptive neural network and local search hybrid approach. Proceedings of the 2006 IEEE Int. Joint Conf. on Neural Networks, pp. 2720-2727, 2006. IEEE Press (DOI: 10.1109/IJCNN.2006.247176 and PDF File).
  93. S. Yang. A comparative study of immune system based genetic algorithms in dynamic environments. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1377-1384, 2006. ACM Press (DOI: 10.1145/1143997.1144209 and PDF File).
  94. S. Yang. Dominance learning in diploid genetic algorithms for dynamic optimization problems. GECCO'06: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 1435-1436, 2006. ACM Press (DOI: 10.1145/1143997.1144232 and PDF File).
  95. S. Yang. Associative memory scheme for genetic algorithms in dynamic environments. EvoWorkshops 2006: Applications of Evolutionary Computing, Lecture Notes in Computer Science, vol. 3907, pp. 788-799, 2006. Springer (DOI: 10.1007/11732242_76 and PDF File).
  96. S. Yang and S. Uyar. Adaptive mutation with fitness and allele distribution correlation for genetic algorithms. Proceedings of the 21st ACM Symposium on Applied Computing (SAC'06), pp. 940-944, 2006. ACM Press (DOI: 10.1145/1141277.1141499 and PDF File).
  97. S. Yang. An improved adaptive neural network for job-shop scheduling. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernatics, Vol. 2, pp. 1200-1205, 2005. IEEE Press (DOI: 10.1109/ICSMC.2005.1571309 and PDF File).
  98. S. Yang. Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2560-2567, 2005. IEEE Press (DOI: 10.1109/CEC.2005.1555015 and PDF File).
  99. R. Tinos and S. Yang. Genetic algorithms with self-organized criticality for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2816-2823, 2005. IEEE Press (DOI: 10.1109/CEC.2005.1555048 and PDF File).
  100. S. Yang. Population-based incremental learning with memory scheme for changing environments. Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 1, pp. 711-718, 2005. ACM Press (DOI: 10.1145/1068009.1068128 and PDF File).
  101. S. Yang. Memory-based immigrants for genetic algorithms in dynamic environments. Proceedings of the 2005 Genetic and Evolutionary Computation Conference, Vol. 2, pp. 1115-1122, 2005. ACM Press (DOI: 10.1145/1068009.1068196 and PDF File). This paper was nominated to the 2005 Genetic and Evolutionary Computation Conference (GECCO-2005) Best Paper Award.
  102. S. Yang. Constructing dynamic test environments for genetic algorithms based on problem difficulty. Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Vol. 2, pp. 1262-1269, 2004. IEEE Press (DOI: 10.1109/CEC.2004.1331042 and PDF File).
  103. S. Yang. Non-stationary problem optimization using the primal-dual genetic algorithm. In R. Sarker, R. Reynolds, H. Abbass, K.-C. Tan, R. McKay, D. Essam and T. Gedeon (editors), Proceedings of the 2003 IEEE Congress on Evolutionary Computation, Vol. 3, pp. 2246-2253, 2003. IEEE Press (DOI: 10.1109/CEC.2003.1299951 and PDF File).
  104. S. Yang and X. Yao. Dual population-based incremental learning for problem optimization in dynamic environments. In M. Gen et. al. (editors), Proceedings of the 7th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 49-56, 2003 (PDF File).
  105. S. Yang. Statistics-based adaptive non-uniform mutation for genetic algorithms. In E. Cantu-Paz, J.A. Foster, K. Deb, L. D. Davis, R. Roy, U.-M. O'Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta. M. A. Potter, A. C. Schultz, K. A. Dowsland, N. Jonoska, and J. Miller (editors), Proceedings of the Genetic and Evolutionary Computation Conference - GECCO 2003, Lecture Notes in Computer Science, vol. 2724, pp. 1618-1619, 2003. Springer (DOI: 10.1007/3-540-45110-2_53).
  106. S. Yang. Adaptive non-uniform crossover based on statistics for genetic algorithms. In W. B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (editors), Proceedings of the 2002 Genetic and Evolutionary Computation Conference, pp. 650-657, 2002. San Francisco, CA: Morgan Kaufmann Publishers (PDF File).
  107. S. Yang. Primal-dual genetic algorithms for royal road functions. In E. F. Camacho, L. Basanez, J. A. de la Puente (editors), Proceedings of the 15th IFAC World Congress, Vol. I: Fuzzy, Neural and Genetic Systems, pp. 373-378, Barcelona, Spain, 21-26 July 2002. Elsevier Science Ltd (DOI: 10.3182/20020721-6-ES-1901.00715).
  108. S. Yang. Statistics-based adaptive non-uniform crossover for genetic algorithms, In J. A. Bullinaria (editor), Proceedings of the 2002 U.K. Workshop on Computational Intelligence (UKCI'02), pp. 201-208, 2002 (PDF File).
  109. S. Yang. Adaptive non-uniform mutation based on statistics for genetic algorithms. In Erick Cantu-Paz (editor), Late-Breaking Papers at the 2002 Genetic and Evolutionary Computation Conference, pp. 490-495, 2002. Menlo Park, CA: AAAI Press (PDF File).
  110. S. Yang. Adaptive crossover in genetic algorithms using statistics mechanism. In R. Standish, M. Bedau and H. Abbass (editors), Proceedings of the 8th Int. Conf. on Artificial Life (ALife VIII), pp. 182-185, 2002. MIT Press (PDF File).
  111. T. Radzik and S. Yang. Experimantal evaluation of algorithmic solutions for generalized network flow models. Presented in the 17th International Symposium on Mathematical Programming (ISMP'00), August 2000.
  112. S. Yang and D. Wang. Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. In H. F. Chen, X. R. Cao, G. Picci and K. J. Hunt (editors), Proceedings of the 14th IFAC World Congress, Vol. J: Discrete Event Systems, Stochastic Systems, Fuzzy and Neural Systems I, pp. 175-180, 1999. Elsevier Science Ltd (PDF File).
  113. K. Zhao, S. Yang and D. Wang. Genetic algorithm and neural network hybrid approach for job-shop scheduling. In M. H. Hamza (editor), Proceedings of the IASTED Int. Conf. on Applied Modelling and Simulation (AMS'98), pp. 110-114, 1998. Calgary, Alberta, Canada: ACTA Press (PDF File).

Non-Refereed Conference Publications

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

Other Workshop Publications

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

PhD Thesis

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

Technical Reports

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

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