http://www.aber.ac.uk/en

He, Jun


Selected Publications

o J. He and X. Yao. Average Drift Analysis and Population Scalability. IEEE Transactions on Evolutionary Computation 21(3): 426 - 439, 2017. (preprint http://cadair.aber.ac.uk/dspace/handle/2160/43794)
o K. Li, Y. Chen, W. Li, J. He and Y. Xue. Improved gene expression programming to solve the inverse problem for ordinary differential equations. Swarm and Evolutionary Computation, 2017.
o D. Corus, J. He, T. Jansen, P. S. Oliveto, D. Sudholt and C. Zarges. On Easiest Functions for Mutation Operators in Bio-Inspired Optimisation. Algorithmica 78(2): 714--740, 2017. (preprint http://cadair.aber.ac.uk/dspace/handle/2160/43623)
o J. He and G. Lin. Average Convergence Rate of Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation 20(2): 316 - 321, 2016. (preprint http://cadair.aber.ac.uk/dspace/handle/2160/43793)
o J. He, T. Chen and X. Yao. On the Easiest and Hardest Fitness Functions. IEEE Transactions on Evolutionary Computation 19(2): 295 - 305, 2015. (preprint http://cadair.aber.ac.uk/dspace/handle/2160/30198)
o X. Lai, Y. R. Zhou, J. He and J. Zhang. Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree Problem. IEEE Transactions on Evolutionary Computation 18(6): 860 - 872, 2014.
o B. Mitavskiy, E. Tuci, C. Cannings, J. Rowe, and J. He. Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and hebbian learning. Natural Computing 12(4): 473- 484, 2013.
o Y. Chen, X. Zou and J. He Drift Conditions for Estimating the First Hitting Times of Evolutionary Algorithm. International Journal of Computer Mathematics 88(1): 37-50, 2011.
o T. Friedrich, J. He , N. Hebbinghaus, F. Neumann and C. Witt. Approximating Covering Problems by Randomized Search Heuristics using Multi-Objective Models. Evolutionary Computation 18(4): 617-633, 2010.
o T. Chen, J. He, G. Chen and X. Yao. Choosing selection pressure for wide-gap problems. Theoretical Computer Science 411(6): 926-934,2010.
o P. S. Oliveto, J. He , and X. Yao. Analysis of the (1+1)-EA for Finding Approximate Solutions to Vertex Cover Problems. IEEE Transactions on Evolutionary Computation 13 (5): 1006 -1029, 2009.
o T. Chen, J. He, G. Sun, G. Chen, and X. Yao, 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 39 (5): 1092-1106, 2009.
o J. He, and L. Kang. A Mixed Strategy of Combining Evolutionary Algorithms with Multigrid Methods. International Journal of Computer Mathematics 86(5): 837-849, 2009.
o Y. Zhou, J. He, and Q. Nie. A Comparative Runtime Analysis of Heuristic Algorithms for Satisfiability Problems. Artificial Intelligence 173(2): 240-257, 2009.
o T. Friedrich, J. He, N. Hebbinghaus, F. Neumann and C. Witt. Analyses of Simple Hybrid Algorithms for the Vertex Cover Problem. Evolutionary Computation 17(1): 3-19, 2009.
o S. Powers and J. He. A Hybrid Artificial Immune System and Self Organising Map for Network Intrusion Detection. Information Sciences 178(15): 3024-3042, 2008.
A. Bennett, R. Johnston, E. Turpin and J. He, Analysis of an Immune Algorithm for Protein Structure Prediction. Informatica 32 (3): 245-251, 2008.
o J. He, C. Reeves, C. Witt and X. Yao. A Note on Problem Difficulty Measures in Black-Box Optimization: Classification, Existence and Predictability. Evolutionary Computation 15 (4): 435-443, 2007
o Y. Zhou and J. He. A Runtime analysis of evolutionary algorithms for constrained optimization problems. IEEE Transactions on Evolutionary Computation 11(5): 608-619, 2007.
o Y. Zhou and J. He. Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids. Information Processing Letters 104(4): 117-122, 2007.
o P. S. Oliveto, J. He, and X. Yao. Time Complexity of Evolutionary Algorithms for Combinatorial Optimization: A Decade of Results. International Journal of Automation and Computing 4 (3): 281-293, 2007.
o H. Dong, J. He, H. Huang and W. Hou. Evolutionary programming using a mixed mutation strategy. Information Sciences 177 (1): 312-327, 2007.
o X. Yao, Y. Liu, J. Li, J. He, and C. Frayn. Current developments and future directions of bio-inspired computation and implications for ecoinformatics. Ecological Informatics 1 (1): 9--22, 2006.
o J. He, X. Yao and J. Li. A Comparative Study of Three Evolutionary Algorithms Incorporating Different Amount of Domain Knowledge for Node Covering Problems. IEEE Transactions on Systems, Man and Cybernetics, Part C 35(2): 266- 271, 2005.
o J. He and X. Yao. A Study of Drift Analysis for Estimating Computation Time of Evolutionary Algorithms. Natural Computing 3 (1): 21-35, 2004.
o J. He and X. Yao. Time complexity analysis of an evolutionary algorithm for finding nearly maximum cardinality matching. Journal of Computer Science and Technology 19 (4): 450--458, 2004.
o J. He and X. Yao. Towards an Analytic Framework for Analysing the Computation Time of Evolutionary Algorithms. Artificial Intelligence 145 (1-2): 59-97, 2003.
o J. He and X. Yao. Drift analysis in studying the convergence and hitting times of evolutionary algorithms: An overview. Wuhan University Journal of Natural Sciences 8(1): 143-154, 2003.
o J. He and X. Yao. 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, 2002.
o J. He and X. Yu. Conditions for the convergence of evolutionary algorithms. Journal of Systems Architecture 47 (7): 601-612, 2001.
o L. Kang, Y. Li, Z. Pan, J. He, and D. J. Evans. Massively parallel algorithms from physics and biology. International Journal of Computer Mathematics 77 (2): 201--250, 2001.
o J. He and X. Yao. Drift Analysis and Average Time Complexity of Evolutionary Algorithms Artificial Intelligence 127 (1):57-85, 2001.
(Erratum in Artificial Intelligence, 140 (1): 245-200, 2002).
J. He, X. Yao and L. Kang. Drift Conditions for Time Complexity of Evolutionary Algorithms. Journal of Software 12 (12): 1775-1783, 2001.
o J. He, J. Xu, and X. Yao. Solving equations by hybrid evolutionary computation techniques. IEEE Transactions on Evolutionary Computation 4 (3): 295-304, 2000.
o J. He and L. Kang. On the convergence rate of genetic algorithms. Theoretical Computer Science 229 (1-2):23-39, 1999.
o J. He, L. Kang and Y. Chen. Multiple structure computational model and its application in optimization. Wuhan University Journal of Natural Sciences 1 (3-4): 593-598, 1996.
o J. He, L. Kang and Y. Chen Convergence of Genetic Evolution Algorithms for Optimization. Parallel Algorithms and Applications 5 (1): 37-56, 1995.

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