Publications

Refereed Journal Articles

  1. Elsayed, S., Sarker, R., Essam, D., Coello Coello, C.: Evolutionary Approach for Large-Scale Mine Scheduling. Information Sciences, vol. 523, pp. 77- 90, 2020 (Q1, IF: 5.910, SNIP: 2.688)

  2. Ahrari, A; Elsayed, S.; Sarker, R; Essam, D., Coello Coello, C.:, Weighted Pointwise Prediction Method for Dynamic Multiobjective Optimization', Information Sciences, vol. 546, pp. 349-367, 2020 (Q1, IF: 5.910, SNIP: 2.688)

  3. Zaman, F; Elsayed, S.; Sarker, R; Essam, D., Coello Coello, C.: An Evolutionary Approach for Resource Constrained Project Scheduling with Uncertain Changes, Computers & Operations Research, in press, 2020. (Q1, IF:3.424, SNIP: 2.289)

  4. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Resource Constrained Project Scheduling with Dynamic Disruption Recovery, IEEE Access, vol. 146, 2020. (Q1, IF:3.557, SNIP: 1.758)

  5. Zaman, F; Elsayed, S.; Sarker, R; Essam, D.: Hybrid evolutionary algorithm for large-scale project scheduling problems, Computers and Industrial Engineering, vol. 146, 2020. (Q1, IF: 3.518, SNIP: 1.755)

  6. Harrison, K.R., Elsayed, S., Garanovich, I., Weir, T., Galister, M., Boswell, S., Taylor, R. and Sarker, R.. Portfolio Optimization for Defence Applications. IEEE Access. pp. 60152 - 60178, 2020 (Q1, IF:3.557, SNIP: 1.758)

  7. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-Assisted Multi-Operator Differential Evolution for Solving Constrained Optimization Problems. Expert Systems With Applications, 2019, in press (Q1, IF:4.29, SNIP: 2.696)

  8. Liu, C., Zhao, Yan, B., Elsayed, S., and Sarker, R.: Transfer Learning-Assisted Multi-Objective Evolutionary Clustering Framework with Decomposition for High-Dimensional Data. Information Sciences, pp. 440-456, 2019 (Q1, IF: 5.910, SNIP: 2.688)

  9. Fernandez-Rojas, R., Perry, A., Singh, H., Campbell, B., Elsayed, S., Hunjet, R., and Abbass, H.: Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey. IEEE Access, pp. 33304 -33328, 2019. [ Download for Free] (Q1, IF:3.557, SNIP: 1.758)

  10. Zaman, M.F., Elsayed, S., Sarker, R., Essam, D., Coello Coello, C.: Multi-Method based Algorithm for Multi-objective Problems under Uncertainty. Information Sciences, 481, 81-109 , 2019. (Q1, IF: 5.910, SNIP: 2.688)

  11. Liu, C., Zhao, Elsayed, S., Ray, T., and Sarker, R: Adaptive sorting-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation, In Press, Accepted 06/2018 (IF:10.629, SNIP:5.404)

  12. Elsayed, S., Sarker, R., Coello Coello, C.: Fuzzy Rule-based Design of Evolutionary Algorithm for Optimization. IEEE Transactions on Cybernetics, accepted 11/2017 (Q1, IF:7.384, SNIP: 2.827)

  13. Elsayed, S., Sarker, R., Coello Coello, C.: Sequence-based Deterministic Initialization for Evolutionary Algorithms. IEEE Transactions on Cybernetics, 47(9), 2911-2923 (2017) (Q1, IF:7.384, SNIP: 2.827)

  14. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Finding Nash Equilibria in Electricity Markets. IEEE Transactions on Evolutionary Computation, accepted, 2017. (Q1, IF:10.629, SNIP: 5.404)

  15. Elsayed, S., Sarker, R., Ray, T., Coello Coello, C.: Consolidated Optimization Algorithm for Resource-constrained Project Scheduling Problems. Information Sciences, 418, 346-368 (2017) (Q1, IF: 5.910, SNIP: 2.688)

  16. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-Based Adaptive Operator Selection Mechanism for Differential Evolution. Information Sciences, 2017, 418, 383-404 (Q1, IF: 5.910, SNIP: 2.688)

  17. Elsayed, S., Sarker, R., Coello Coello, C., Ray, T.: Adaptation of Operators and Continuous Control Parameters in Differential Evolution for Constrained Optimization. Soft Computing, 2017, in press. (Q2, IF:2.472, SNIP: 1.204)

  18. Shafie, K., Elsayed, S., Sarker, R., Ryan, M.: Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms. Applied Soft Computing, 56, 717-729, 2017. (Q1, IF:3.541, SNIP: 2.037)

  19. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Co-evolutionary Approach for Strategic Bidding in Competitive Electricity Markets. Applied Soft Computing, 51, 1-22 (2017). (Q1, IF:3.541, SNIP: 2.037)

  20. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Power Generation Planning with Uncertain Renewable Energy. Energy, 112, 408-419 (2016). (Q1, IF:4.52, SNIP: 1.798)

  21. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Configuring Two-algorithm-based Evolutionary Approach for Solving Dynamic Economic Dispatch Problems. Engineering Applications of Artificial Intelligence, 53, 105-125 (2016). (Q1, IF:2.894, SNIP: 1.940)

  22. Elsayed, S., Sarker, R.: Differential evolution framework for big data optimization. Memetic Computing, 8 (1), 17-33, (2016). (Q2, IF:2.205, SNIP: 1.336)

  23. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: Evolutionary Algorithms for Dynamic Economic Dispatch Problems. IEEE Transactions on Power Systems, 1-10 (2015). (Q1, IF:5.68, SNIP: 3.624)

  24. ayed, E., Essam, D., Sarker, R., Elsayed, S.: Decomposition-based evolutionary algorithm for large scale constrained problems. Information Sciences, 316, 457-486 (2015). (Q1, IF: 5.910, SNIP: 2.688)

  25. Mabrok, M.A., Elsayed, S., Ryan, M.J.: Mathematical framework for recursive model-based system design. Nonlinear Dynamics, 84, 223–236 (2016). (Q1, IF:3.464, SNIP: 1.523)

  26. Elsayed, S., Sarker, R., Essam, D.: Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization. Applied Soft Computing, 26, 515-522 (2015). (Q1, IF:3.541, SNIP: 2.037)

  27. Elsayed, S., Sarker, R., Essam, D.: Survey of Uses of Evolutionary Computation Algorithms and Swarm Intelligence for Network Intrusion Detection. International Journal of Computational Intelligence and Applications, 14(4) (2015) (SNIP: 0.350).

  28. Sarker, R., Elsayed, S., Ray, T.: Differential evolution with dynamic parameters selection for optimization problems. IEEE Transactions on Evolutionary Computation 18(5), 689-707 (2014). (Q1, IF:10.629, SNIP: 5.404)

  29. Elsayed, S., Sarker, R., Mezura-Montes, E.: Self-adaptive mix of particle swarm methodologies for constrained optimization. Information Sciences, 27, 216-233 (2014). (Q1, IF: 5.910, SNIP: 2.688)

  30. Hamza, N., Sarker, R., Essam, D., Deb, K., Elsayed, S.: A constraint consensus memetic algorithm for solving constrained optimization problems. Engineering Optimization, 46(11), 1447-1464 (2014). (Q2, IF:1.380, SNIP: 1.149)

  31. Sarker, R., Elsayed, S.: Evolutionary algorithm for analyzing higher degree research student recruitment and completion. Cogent Engineering, 2(1), 1063760 (2015) (SNIP: 0.507).

  32. Elsayed, S., Sarker, R., Essam, D.L.: A self-adaptive combined strategies algorithm for constrained optimization using differential evolution. Applied Mathematics and Computation, 241, 267-282 (2014). (Q1, IF:1.738, SNIP: 1.203)

  33. Elsayed, S., Sarker, R., Essam, D.L.: A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence, 27(0), 57-69 (2014). (Q1, IF:2.894, SNIP: 1.940)

  34. Elsayed, S., Sarker, R., Essam, D.L.: Adaptive Configuration of evolutionary algorithms for constrained optimization. Applied Mathematics and Computation, 222, 680-711 (2013). (Q1, IF:1.738, SNIP: 1.203)

  35. Elsayed, S., Sarker, R., Essam, D.L.: Self-adaptive differential evolution incorporating a heuristic mixing of operators. Computational Optimization and Applications, 54(3), 771-790 (2013). (Q1, IF:1.52, SNIP: 1.323)

  36. Elsayed, S., Sarker, R., Essam, D.L.: An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems. IEEE Transactions on Industrial Informatics, 9(1), 89-99 (2013). (Q1, IF:6.764, SNIP: 3.339)

  37. Elsayed, S., Sarker, R., Essam, D.: On an Evolutionary Approach for Constrained Optimization Problem Solving. Applied Soft Computing, 12(10), 3208-3227 (2012). (Q1, IF:3.541, SNIP: 2.037)

  38. Elsayed, S., Sarker, R., Essam, D.L.: Multi-operator based evolutionary algorithms for solving constrained optimization problems. Computers and Operations Research, 38(12), 1877-1896 (2011). (Q1, IF:2.600, SNIP: 2.151)

Edited Books

  1. Leu, G., Singh, H., Elsayed, S. (Eds.). (2016). Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium, IES 2016, Canberra, Australia, November 2016, Proceedings (Vol. 8). Springer

Scholarly book chapters

40. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Differential Evolution with Landscape-based Operator Selection for Solving numerical optimization problems. In: Leu, G., Singh, H., Elsayed, S. (eds.) Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium. Proceedings in Adaptation, Learning and Optimization, pp. 371-387. Springer International Publishing.

41. Zaman, F., Elsayed, S., Ray, T., Sarker, R.: An Evolutionary Framework for the Bi-Objectives Dynamic Economic and Environmental Dispatch Problems. In: Leu, G., Singh, H., Elsayed, S. (eds.) Intelligent and Evolutionary Systems: The 20th Asia Pacific Symposium. Proceedings in Adaptation, Learning and Optimization, pp. 495-508. Springer International Publishing.

42. Elsayed, S., Sarker, R.: Dynamic Configuration of Differential Evolution Control Parameters and Operators. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 78-88. Springer International Publishing, Cham (2016)

43. Zaman, M.F., Elsayed, S., Ray, T., Sarker, R.: A Double Action Genetic Algorithm for Scheduling the Wind-Thermal Generators. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 258-269. Springer International Publishing, Cham (2016)

44. Elsayed, S., Zaman, M.F., Sarker, R.: Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, H.J. (eds.) Intelligent and Evolutionary Systems: The 19th Asia Pacific Symposium, IES 2015. Proceedings in Adaptation, Learning and Optimization, vol. 5, pp. 357-369. Springer International Publishing, Cham (2016)

45. Debie, E., Elsayed, S., Essam, D.L., Sarker, R.: Investigating Multi-Operator Differential Evolution for Feature Selection. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016. Lecture Notes in Computer Science, pp. 273-284. Springer International Publishing, Cham (2016)

46. Ali, I.M., Elsayed, S., Ray, T., Sarker, R.: A Differential Evolution Algorithm for Solving Resource Constrained Project Scheduling Problems. In: Ray, T., Sarker, R., Li, X. (eds.) Artificial Life and Computational Intelligence: Second Australasian Conference, ACALCI 2016, Canberra, ACT, Australia, February 2-5, 2016, Proceedings. Lecture Notes in Computer Science, pp. 209-220. Springer International Publishing, Cham (2016)

47. Elsayed, S., Sarker, R.: Evolving the Parameters of Differential Evolution Using Evolutionary Algorithms. In: Handa, H., Ishibuchi, H., Ong, Y.-S., Tan, K.C. (eds.) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 1. Proceedings in Adaptation, Learning and Optimization, pp. 523-534. Springer International Publishing, (2015)

48. Elsayed, S., Sarker, R., Essam, D.: The Influence of the Number of Initial Feasible Solutions on the Performance of an Evolutionary Optimization Algorithm. In: Bui, L., Ong, Y., Hoai, N., Ishibuchi, H., Suganthan, P. (eds.) Simulated Evolution and Learning, vol. 7673. Lecture Notes in Computer Science, pp. 1-11. Springer Berlin Heidelberg, (2012)

49. Elsayed, S., Sarker, R., Essam, D.: A Three-Strategy Based Differential Evolution Algorithm for Constrained Optimization. In: Wong, K., Mendis, B.S., Bouzerdoum, A. (eds.) Neural Information Processing. Theory and Algorithms, vol. 6443. Lecture Notes in Computer Science, pp. 585-592. Springer Berlin Heidelberg, (2010)

50. Elsayed, S., Sarker, R., Essam, D.: A Comparative Study of Different Variants of Genetic Algorithms for Constrained Optimization. In: Deb, K., Bhattacharya, A., Chakraborti, N., Chakroborty, P., Das, S., Dutta, J., Gupta, S., Jain, A., Aggarwal, V., Branke, J., Louis, S., Tan, K. (eds.) Simulated Evolution and Learning, vol. 6457. Lecture Notes in Computer Science, pp. 177-186. Springer Berlin / Heidelberg, (2010)

Refereed conference papers

51. El-Fiqi, H.; Campbell, B.; Elsayed, S.; Perry, A.; Singh, H.; Hunjet, R.; Abbass, H: A preliminary study towards an improved shepherding model. Genetic and Evolutionary Computation Conference Companion (GECCO ’20). Mexico, 2020

52. Elsayed, S.; Sarker, R., Hamza, N., Coello Coello, C., Mezura-Montes, E.: Enhancing Evolutionary Algorithms by Efficient Population Initialization for Constrained Problems', IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020

53. Sallam, K; Elsayed S.; Chakrabortty, R.; Ryan, M.: Improved Multi-operator Differential Evolution Algorithm for Solving Unconstrained Problems, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020

54. Sallam, K.; Elsayed, S.; Chakrabortty, R.; Ryan, M., 2020: Multi-Operator Differential Evolution Algorithm for Solving Real-World Constrained Optimization Problems, IEEE World Congress on Computational Intelligence (WCCI) 2020, Glasgow, UK, 19 July 2020 - 24 July 2020

55. Ahrari A; Elsayed S; Sarker R; Essam D, 2019, 'A New Prediction Approach for Dynamic Multiobjective Optimization', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 2268 - 2275

56. Zaman F; Elsayed S; Sarker R; Essam D; Coello Coello CA, 2019, 'Evolutionary Algorithm for Project Scheduling under Irregular Resource Changes', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 403 - 410

57. Singh H; Campbell B; Elsayed S; Perry A; Hunjet R; Abbass H, 2019, 'Modulation of Force Vectors for Effective Shepherding of a Swarm: A Bi-Objective Approach', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 2941 - 2948

58. Li K; Elsayed S; Sarker R; Essam D, 2019, 'Quantum Differential Evolution: An Investigation', in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, pp. 3022 - 3029

59. Liu, C., Zhao, Q., Yan, B., Elsayed, S. and Sarker, R., 2018, December. An Improved Multi-Objective Evolutionary Approach for Clustering High-Dimensional Data. In: 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), Zurich, Switzerland , 2018 pp. 184-190.

60. Meselhi M; Sarker R; Essam D; Elsayed S, 2018, 'Decomposition of overlapping optimization functions', in Proceedings of International Conference on Computers and Industrial Engineering, CIE, Auckland, New Zealand, presented at CIE 48, Computers & Industrial Engineering, Auckland, New Zealand, 02 December 2018 - 05 December 2018

61. 45. Meselhi, M., Essam D., Sarker, R., Elsayed, S.: Enhanced Differential Grouping for Large Scale Optimization. In: 10th International Joint Conference on Computational Intelligence (IJCCI2018), 06 - 08 December, 2018, Seville, Spain, in press

62. Zaman, F., Elsayed, S., Sarker, R., Essam, D.: Scenario based Solution Approach for Uncertain Resource Constrained Scheduling Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8

63. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Landscape-based Differential Evolution for Constrained Optimization Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8

64. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Improved United Multi-Operator algorithm for Solving Optimization Problems. In: IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brazil, 2018, pp. 1-8

65. Meselhi, M., Elsayed, S., Essam D., Sarker, R.: Fast Differential Evolution for Big Optimization. In: The 11th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2017), 2017, Colombo, Sri Lanka, pp. 1-6

66. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: Multi-method based Orthogonal Experimental Design Algorithm for Solving CEC2017 Competition Problems. In: IEEE Congress on Evolutionary Computation, Donostia - San Sebastián, Spain, 2017, pp. 1350-1357

67. Sallam, K., Elsayed, S., Sarker, R., Essam, D.: A Two-phase Differential Evolution Framework for Solving Real-World Application Problems. The 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), Athena, Greece, pp. 1-8.

68. Elsayed, S., Sarker, R., Coello, C. C.: Enhanced Multi-operator Differential Evolution for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 4191-4198.

69. Elsayed, S., Hamza, N., Sarker, R.: Testing United Multi-operator Evolutionary Algorithms-II on Single Objective Optimization Problems. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 2966-2973. Received Best Algorithm Award.

70. Zaman, F., Elsayed, S., Sarker, R.: A Co-evolutionary approach for optimal bidding strategy of multiple electricity suppliers. In: IEEE Congress on Evolutionary Computation, Vancouver, 2016, pp. 3407-3715.

71. Aguilar-Justo, A., Mezura-Montes, E., Elsayed, S., Sarker, R.: Genetic-based search for decomposition based on Variable Interaction Identification for Constrained Problems. 2016 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC 2016), Ixtapa, Mexico, 2016, pp. 1-6.

72. Elsayed, S., Sarker, R., Slay, J.: Evaluating the performance of a differential evolution algorithm in anomaly detection. In: IEEE Congress on Evolutionary Computation, 2015, pp. 2490-2497.

73. Elsayed, S., Sarker, R.: An Adaptive Configuration of Differential Evolution Algorithms for Big Data. In: IEEE Congress on Evolutionary Computation 2015, pp. 695-702.

74. Ali, I., Elsayed, S., Ray, T., Sarker, R.: Memetic algorithm for solving resource constrained project scheduling problems. In: IEEE Congress on Evolutionary Computation, 2015, pp. 2761-2767.

75. Sallam, K.M., Sarker, R., Essam, D.L., Elsayed, S.: Neurodynamic differential evolution algorithm and solving CEC2015 competition problems. In: IEEE Congress on Evolutionary Computation, 2015, pp. 1033-1040.

76. Zaman, M. D., Elsayed, S., Ray, T., Sarker, R.: An Evolutionary Approach for Scheduling Solar-thermal Power Generation System. In: International Conference on Computers & Industrial Engineering, 2015, 45, pp.1-8.

77. Sayed, E., Essam, D., Sarker, R., Elsayed, S.: A decomposition-based algorithm for dynamic economic dispatch problems. In: IEEE Congress on Evolutionary Computation 2014, pp. 1898-1905.

78. Greenwood, G.W., Elsayed, S., Sarker, R., Abbass, H.A.: Online generation of trajectories for autonomous vehicles using a multi-agent system. In: IEEE Congress on Evolutionary Computation, Beijing, 2014, pp. 1218-1224.

79. Elsayed, S., Sarker, R., Essam, D.L., Hamza, N.M.: Testing united multi-operator evolutionary algorithms on the CEC2014 real-parameter numerical optimization. In: IEEE Congress on Evolutionary Computation, Beijing, 2014, pp. 1650-1657. Received Best Algorithm Award.

80. Elsayed, S., Sarker, R., Essam, D.L.: United multi-operator evolutionary algorithms. In: IEEE Congress on Evolutionary Computation 2014, pp. 1006-1013.

81. Elsayed, S., Ray, T., Sarker, R.: A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems. In: IEEE Congress on Evolutionary Computation, 2014, pp. 1062-1068.

82. Elsayed, S., Sarker, R.: Differential Evolution with automatic population injection scheme for constrained problems. In: IEEE Symposium Series on Computational Intelligence, Singapore, 2013, pp. 112-118.

83. Elsayed, S., Sarker, R., Ray, T.: Differential Evolution with Automatic Parameter Configuration for Solving the CEC2013 Competition on Real-Parameter Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 1932-1937.

84. Elsayed, S., Sarker, R., Mezura-montes, E.: Particle Swarm Optimizer for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 1703-1711.

85. Elsayed, S., Sarker, R., Essam, D.: A Genetic Algorithm for Solving the CEC'2013 Competition Problems on Real-Parameter Optimization. In: IEEE Congress on Evolutionary Computation, Cancún, México, 2013, pp. 356-360.

86. Elsayed, S., Sarker, R., Ray, T.: Parameters Adaptation in Differential Evolution. In: IEEE Congress on Evolutionary Computation, Brisbane 2012, pp. 1- 8.

87. Elsayed, S., Sarker, R., Essam, D.: Memetic Multi-Topology Particle Swarm Optimizer for Constrained Optimization. In: IEEE Congress on Evolutionary Computation, World Congress on Computational Intelligence (WCCI2012), Brisbane 2012, pp. 1-8.

88. Elsayed, S., Sarker, R., Essam, D.: Improved genetic algorithm for constrained optimization. In: International Conference on Computer Engineering and Systems, ICCES'2011, Cairo, Egypt, 2011, pp. 111-115.

89. Hamza, N., Elsayed, S., Essam, D., Sarker, R.: Differential evolution combined with constraint consensus for constrained optimization. In: IEEE Congress of Evolutionary Computation, New Orleans, LA, 2011 2011, pp. 865-872.

90. Elsayed, S., Sarker, R., Essam, D.L.: GA with a new multi-parent crossover for constrained optimization. In: IEEE Congress on Evolutionary Computation 2011, pp. 857-864.

91. Elsayed, S., Sarker, R., Essam, D.L.: Integrated strategies differential evolution algorithm with a local search for constrained optimization. In: IEEE Congress on Evolutionary Computation2011, pp. 2618-2625.

92. Elsayed, S., Sarker, R., Essam, D.L.: GA with a new multi-parent crossover for solving IEEE-CEC2011 competition problems. In: IEEE Congress on Evolutionary Computation 2011, pp. 1034-1040. Received Best Algorithm Award.

93. Elsayed, S., Sarker, R., Essam, D.: Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems. In: IEEE Congress of Evolutionary Computation, 2011, pp. 1041-1048.

94. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: A Hybrid Genetic Scatter Search Algorithm for Solving Optimization Problems. In: the 6th International Conference on Informatics and Systems, Cairo, Egypt, pp. DS-12: DS1-7, 2008.

95. Elsayed, S., Abd EL-Wahed, W., Ismail, N.: Parallel Scatter Search Algorithm for Solving Optimization Problems. In: the 17th International Conference on Computer Theory and Applications (ICCTA'2007), Alexandria, Egypt, September 2007.