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Shengxiang Yang's Selected Publications

Since 1st July, 2010, I have moved to the Department of Information Systems and Computing, Brunel University, Uxbridge UB8 3PH, UK. My new Publications Page can be found here.

Edited Books, Proceedings and Journal Special Issues

  1. 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-Verlag Berlin Heidelberg, ISSN: 1860-949X (Print) 1860-9503 (Online), ISBN: 978-3-540-49772-1, March 2007 (DOI: 10.1007/978-3-540-49774-5).
  2. 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).
  3. 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).
  4. 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).
  5. 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).
  6. 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 version), ISSN: 1865-9292 (electronic version).
  7. 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).

Journal Papers (and Source Codes)

  1. S. Yang and C. Li. A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation, accepted in November 2009. IEEE Press (DOI: 10.1109/TEVC.2010.2046667, PDF File, and Source Code in GNU C++).
  2. 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, published online first: 30 June 2010. Elsevier (PDF File, and Source Code in C++).
  3. 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, published online first: 3 June 2010. IEEE Press (DOI: 10.1109/TSMCC.2010.2049200, PDF File).
  4. 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, published online first: 5 April 2010. IEEE Press (DOI: 10.1109/TSMCB.2010.2043527, PDF File).
  5. X. Peng, X. Gao, and S. Yang. Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments. Soft Computing, published online first: 11 February 2010. Springer (DOI: 10.1007/s00500-010-0547-5, PDF File).
  6. H. Wang, S. Yang, W. H. Ip, and D. Wang. A memetic algorithm based on particle swarm optimization for dynamic optimization problems. Natural Computing, 9(3): 703-725, September 2010. Springer (DOI: 10.1007/s11047-009-9176-2, PDF File).
  7. 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, PDF File).
  8. 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).
  9. 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, PDF File).
  10. 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, PDF File).
  11. 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++).
  12. 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, PDF File).
  13. 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, PDF File).
  14. 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).
  15. 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, PDF File).
  16. 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, PDF File).
  17. 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++).
  18. 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++).
  19. 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, PDF File).
  20. 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++).
  21. H. Wang, D. Wang, and S. Yang. Evolutionary algorithms in dynamic environments. Control and Decision, 22(2): 127-131+137, February 2007.
  22. 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, PDF File).
  23. 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, PDF File).
  24. S. Yang. Adaptive group mutation for tackling deception in genetic search. WSEAS Transactions on Systems, 3(1): 107-112, January 2004 (PDF File).
  25. 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, PDF File).
  26. 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, PDF File).
  27. 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.
  28. 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.
  29. 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.
  30. S. Yang and D. Wang. Solving optimization and scheduling problems with neural network methods. Systems Engineering, 15(Suppl.): 66-71, December 1997.

Book Chapters

  1. 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, (ISBN: 978-3-642-13424-1, PDF File).
  2. 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, Nova Science Publishers, 2009 (ISBN: 978-1-60741-101-7, PDF File).
  3. 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, Chapter 1, pp. 3 - 28, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_1, PDF File).
  4. 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, Chapter 5, pp. 105 - 127, Springer-Verlag Berlin Heidelberg, March 2007 (DOI: 10.1007/978-3-540-49774-5_5, PDF File).
  5. S. Yang. Adaptive mutation using statistics mechanism for genetic algorithms. In F. Coenen, A. Preece and A. Macintosh (editors), Research and Development in Intelligent Systems XX, pp. 19-32, 2003. London: Springer-Verlag (PDF File).
  6. S. Yang. PDGA: the primal-dual genetic algorithm. In A. Abraham, M. Koppen and K. Franke (editors), Design and Application of Hybrid Intelligent Systems, pp. 214-223, 2003. IOS Press (PDF File).
  7. S. Yang. Genetic algorithms based on primal-dual chromosomes for royal road functions. In A. Grmela and N. E. Mastorakis (editors), Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 174-179, 2002. WSEAS Press (PDF File).

Conference Papers

  1. 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, 2010. Springer (PDF File)
  2. 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, 2010. Springer (PDF File)
  3. 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, 2010. IEEE Press (PDF File).
  4. C. Li and S. Yang. Adaptive learning particle swarm optimizer-II for function optimization. Proceedings of the 2010 IEEE Congress on Evolutionary Computation, 2010. IEEE Press (PDF File).
  5. 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, 2010. IEEE Press (PDF File).
  6. I. Korejo, S. Yang, and C. Li. A directed mutation operator for real coded genetic algorithms. EvoApplications 2010: Applications of Evolutionary Computing, Part I, LNCS 6024, pp. 491-500, 2010. Springer (DOI: 10.1007/978-3-642-12239-2_51, PDF File).
  7. 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, LNCS 6024, pp. 562-571, 2010. Springer (DOI: 10.1007/978-3-642-12239-2_58, PDF File).
  8. I. Korejo, S. Yang, and C. Li. A comparative study of adaptive mutation operators for metaheuristics. Proceedings of the 8th Metaheuristic International Conference, 2009.
  9. 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).
  10. 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 (PDF File).
  11. 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, PDF File).
  12. 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, PDF File).
  13. 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, PDF File).
  14. 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, PDF File).
  15. 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, PDF File).
  16. 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, LNCS 5484, pp. 725-734, 2009. Springer (DOI: 10.1007/978-3-642-01129-0_82, PDF File).
  17. C. Li and S. Yang. An island based hybrid evolutionary algorithm for optimization. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 180-189, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_19, PDF File).
  18. H. Cheng and S. Yang. Joint multicast routing and channel assignment in multiradio multichannel wireless mesh networks using simulated annealing. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 370-380, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_38, PDF File).
  19. C. Li and S. Yang. A generalized approach to construct benchmark problems for dynamic optimization. Proceedings of the 7th Int. Conf. on Simulated Evolution and Learning, LNCS 5361, pp. 391-400, 2008. Springer (DOI: 10.1007/978-3-540-89694-4_40, PDF File).
  20. S. N. Jat and S. Yang. A memetic algorithm for the university course timetabling problem. Proceedings of the 20th IEEE Int. Conf. on Tools with Artificial Intelligence, vol. 1, pp. 427-433, 2008. IEEE Press (DOI: 10.1109/ICTAI.2008.126, PDF File).
  21. 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, PDF File).
  22. 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, PDF File).
  23. 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, PDF File).
  24. 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, LNCS 5199, pp. 909-918, 2008. Springer (DOI: 10.1007/978-3-540-87700-4_90, PDF File).
  25. 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).
  26. 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).
  27. R. Tinos and S. Yang. Evolutionary programming with q-Gaussian mutation for dynamic optimization problems. Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pp. 1823-1830, 2008. IEEE Press (DOI: 10.1109/CEC.2008.4631036, PDF File).
  28. 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, PDF File).
  29. 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, PDF File).
  30. H. Richter and S. Yang. Memory based on abstraction for dynamic fitness functions. In EvoWorkshops 2008: Applications of Evolutionary Computing, LNCS 4974 , pp. 597-606, 2008. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-78761-7_65, PDF File).
  31. L. Liu, D. Wang, and S. Yang. Compound particle swarm optimization in dynamic environments. In EvoWorkshops 2008: Applications of Evolutionary Computing, LNCS 4974, pp. 617-626, 2008. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-78761-7_67, PDF File).
  32. 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, PDF File).
  33. 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, PDF File).
  34. S. Yang. Learning the dominance in diploid genetic algorithms for changing optimization problems. Proceedings of the 2nd Int. Symp. on Intelligence Computation and Applications, pp. 157-162, 2007. China University of GeoSciences Press (PDF File).
  35. S. Yang. Genetic algorithms with elitism-based immigrants for changing optimization problems. EvoWorkshops 2007: Applications of Evolutionary Computing, LNCS 4448, pp. 627-636, 2007. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-71805-5_69, PDF File).
  36. H. Wang, D. Wang, and S. Yang. Triggered memory-based swarm optimization in dynamic environments. EvoWorkshops 2007: Applications of Evolutionary Computing, LNCS 4448, pp. 637-646, 2007. Berlin: Springer-Verlag (DOI: 10.1007/978-3-540-71805-5_70, PDF File).
  37. 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, PDF File).
  38. 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 (PDF File).
  39. 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, PDF File).
  40. 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, PDF File).
  41. S. Yang. Associative memory scheme for genetic algorithms in dynamic environments. EvoWorkshops 2006: Applications of Evolutionary Computing, LNCS 3907, pp. 788-799, 2006. Berlin: Springer-Verlag (DOI: 10.1007/11732242_76, PDF File).
  42. 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, PDF File).
  43. S. Yang. An improved adaptive neural network for job-shop scheduling. Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 2, pp. 1200-1205, 2005. IEEE Press (DOI: 10.1109/ICSMC.2005.1571309, PDF File).
  44. 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, PDF File).
  45. 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, PDF File).
  46. 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, PDF File).
  47. 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, PDF File). This paper was nominated to the best paper award for GECCO-2005.
  48. 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, PDF File).
  49. 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, PDF File).
  50. 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, PDF File).
  51. 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).
  52. 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, LNCS 2724, pp. 1618-1619, 2003. Springer (DOI: 10.1007/3-540-45110-2_53, PDF File).
  53. 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).
  54. 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 (PDF File).
  55. 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).
  56. 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).
  57. 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).
  58. T. Radzik and S. Yang. Experimental evaluation of algorithmic solutions for generalized network flow models. Presented in the 17th International Symposium on Mathematical Programming (ISMP'00), August 2000 (PDF File).
  59. 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).
  60. 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).

Other Workshop Publications

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

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. 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., 2008 (PDF File).
  2. 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 (PDF File).
  3. 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 (PDF File).

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