Publication

Journal Articles

• 1. Raja, T. V., Ezziane, Z., He, J., Ma, X., & Wali-Zubair Kazaure, A. (2026). Identification and detection of DDoS attack on smart home infrastructure using machine learning models. Scientific Reports.

  2. He, J., Chong, S. Y., & Yao, X. (2025). Estimation of Hitting Time by Hitting Probability for Elitist Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation.

  3. Rai, T., He, J., Kaur, J., Shen, Y., Mahmud, M., Brown, D.J., O'Dowd, E. and Baldwin, D. (2025). Evaluating XAI techniques under class imbalance using CPRD data. Frontiers in Artificial Intelligence.

  4. Huang, W., He, J., & Zhu, L. (2025). A multiple direction search algorithm for continuous optimization. Swarm and Evolutionary Computation.

  5. Xu, T., Chen, H., & He, J. (2024). An Adaptive Helper and Equivalent Objective Evolution Strategy for Constrained Optimization. Information Sciences.

  6. He, J. & Zhou, Y. (2023). Drift Analysis with Fitness Levels for Elitist  Evolutionary Algorithms. Evolutionary Computation. 

  7. Wang, C., He, J., Chen, Y., & Zou, X. (2022). Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms. Mathematics, 10(16).

  8. Chen. Y. & He, J. (2021). Average Convergence Rate of Evolutionary Algorithms in Continuous Optimization.  Information Sciences, 562, 200-219.

  9. Wang, C., Chen, Y., He, J., & Xie, C. (2021). Error analysis of elitist randomized search heuristics. Swarm and Evolutionary Computation, 63, 100875.

  10. Xu, T., He, J., & Shang, C. (2022). Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization IEEE Transactions on Cybernetics, 52(1), 240-251.

  11. Chong, S. Y., Tiňo, P., & He, J. (2019). Coevolutionary systems and PageRank. Artificial Intelligence.

  12. Huang, W., Xu, T., Li, K., & He, J. (2019). Multiobjective differential evolution enhanced with principle component analysis for constrained optimization. Swarm and Evolutionary Computation

  13. Ding, R., Dong, H., He, J., & Li, T. (2019). A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference points. Applied Soft Computing, 78, 447-464.

  14. Pang, J., He, J., & Dong, H. (2018). Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method. Soft Computing.

  15. Zhou, Y., Xiang, Y., Chen, Z., He, J., & Wang, J. (2019). A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization Problems. IEEE Transactions on Cybernetics, 49(6), 2073-2084.

  16. Chong, S. Y., Tiňo, P., He, J., & Yao, X. (2019). A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks. Evolutionary Computation, 27(2), 195-228.

  17. Li, K., Chen, Y., Li, W., He, J., & Xue, Y. (2018). Improved gene expression programming to solve the inverse problem for ordinary differential equations. Swarm and Evolutionary Computation, 38, 231-239.

  18. He, J., & Yao, X. (2017). Average drift analysis and population scalability. IEEE Transactions on Evolutionary Computation, 21(3), 426-439.  

  19. Corus, D., He, J., Jansen, T., Oliveto, P. S., Sudholt, D., & Zarges, C. (2017). On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica, 78(2), 714-740. 

  20. He, J., & Lin, G. (2016).  Average convergence rate of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 20(2), 316-321.  

  21. He, J., Chen, T., & Yao, X. (2015). On the easiest and hardest fitness functions. IEEE Transactions on Evolutionary Computation, 19(2), 295-305.  

  22. Lai, X., Zhou, Y., He, J., & Zhang, J. (2014). Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree Problem. IEEE Transactions on Evolutionary Computation, 18(6), 860-872.  

  23. Mitavskiy, B. S., Tuci, E., Cannings, C., Rowe, J., & He, J. (2013). Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning. Natural Computing, 12(4), 473-484 

  24. Chen, Y., Zou, X., & He, J. (2011). Drift conditions for estimating the first hitting times of evolutionary algorithms. International Journal of Computer Mathematics, 88(1), 37-50.

  25. Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2010). Approximating Covering Problems by Randomized Search Heuristics using Multi-Objective Models. Evolutionary Computation, 18(4), 617-633. 

  26. Chen, T., He, J., Chen, G., & Yao, X. (2010).  Choosing selection pressure for wide-gap problems. Theoretical Computer Science, 411(6), 926-934.

  27. Oliveto, P. S., He, J., & Yao, X. (2009). Analysis of the (1+ 1)-EA for finding approximate solutions to vertex cover problems. IEEE Transactions on Evolutionary Computation, 13(5), 1006-1029. 

  28. Chen, T., He, J., Sun, G., Chen, G., & Yao, X. (2009). A new approach for analyzing average time complexity of population-based evolutionary algorithms on unimodal problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(5), 1092-1106. 

  29. He, J., & Kang, L. (2009). A mixed strategy of combining evolutionary algorithms with multigrid methods. International Journal of Computer Mathematics, 86(5), 837-849.

  30. Zhou, Y., He, J., & Nie, Q. (2009). A comparative runtime analysis of heuristic algorithms for satisfiability problems. Artificial Intelligence, 173(2), 240-257.

  31. Friedrich, T., He, J., Hebbinghaus, N., Neumann, F., & Witt, C. (2009). Analyses of simple hybrid algorithms for the vertex cover problem. Evolutionary Computation, 17(1), 3-19.

  32. Powers, S. T., & He, J. (2008). A hybrid artificial immune system and Self Organising Map for network intrusion detection. Information Sciences, 178(15), 3024-3042.  

  33. Bennett, A. J., Johnston, R. L., Turpin, E., & He, J.  (2008). Analysis of an immune algorithm for protein structure prediction. Informatica, 32(3), 245-251.

  34. He, J., Reeves, C., Witt, C., & Yao, X. (2007). A note on problem difficulty measures in black-box optimization: Classification, realizations and predictability. Evolutionary Computation, 15(4), 435-443.

  35. Zhou, Y., & He, J. (2007). A runtime analysis of evolutionary algorithms for constrained optimization problems. IEEE Transactions on Evolutionary Computation, 11(5), 608-619.

  36. Zhou, Y., & He, J. (2007). Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids. Information Processing Letters, 104(4), 117-122.

  37. Oliveto, P. S., He, J., & Yao, X. (2007). Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results. International Journal of Automation and Computing, 4(3), 281-293.

  38. Dong, H., He, J., Huang, H., & Hou, W. (2007). Evolutionary programming using a mixed mutation strategy. Information Sciences, 177(1), 312-327.

  39. Yao, X., Liu, Y., Li, J., He, J., & Frayn, C. (2006). Current developments and future directions of bio-inspired computation and implications for ecoinformatics. Ecological Informatics, 1(1), 9-22.

  40. He, J., Yao, X., & Li, J. (2005). A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(2), 266-271.

  41. He, J., & Yao, X. (2004). A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing, 3(1), 21-35.

  42. He, J., & Yao, X. (2004). Time complexity analysis of an evolutionary algorithm for finding nearly maximum cardinality matching. Journal of Computer Science and Technology, 19(4), 450-458.

  43. He, J., & Yao, X. (2003). Drift analysis in studying the convergence and hitting times of evolutionary algorithms: An overview. Wuhan University Journal of Natural Sciences, 8(1), 143-154.

  44. He, J., & Yao, X. (2003). Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence, 145(1-2), 59-97.

  45. He, J., & Yao, X. (2002). From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 6(5), 495-511.

  46. He, J., & Yu, X. (2001). Conditions for the convergence of evolutionary algorithms. Journal of Systems Architecture, 47(7), 601-612.

  47. Kang, L., Li, Y., Pan, Z., He, J., & Evans, D. J. (2001). Massively parallel algorithms from physics and biology. International Journal of Computer Mathematics, 77(2), 201-250.

  48. He, J., & Yao, X. (2001). Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence, 127(1), 57-85. (Erratum in Artificial Intelligence, 140 (1), 245-200, 2002).

  49. He, J., & Yao, X., & Kang, L. (2001). Drift Conditions for Time Complexity of Evolutionary Algorithms. Journal of Software 12(12), 1775-1783.

  50. He, J., Xu, J., & Yao, X. (2000). Solving equations by hybrid evolutionary computation techniques. IEEE Transactions on Evolutionary Computation, 4(3), 295-304.

  51. He, J., & Kang, L. (1999).  On the convergence rate of genetic algorithms. Theoretical Computer Science, 229(1), 23-39.

  52. He, J., Kang, L. S., & Chen, Y. J. (1996). Multiple structure computational model and its application in optimization. Wuhan University Journal of Natural Sciences, 1(3), 593-598.

  53. He, J., Kang, L. S., & Chen, Y. J. (1995). Convergence of Genetic Evolution Algorithms for Optimization. Parallel Algorithms and Applications, 5(1-2), 37-56.

Author IDs

• • Google Scholar

  • ORCID ID: 0000-0002-5616-4691