
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) 

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. 

D. Corus, J. He, T.
Jansen, P. S. Oliveto, D. Sudholt and C. Zarges. On
Easiest Functions for Mutation Operators in BioInspired
Optimisation. Algorithmica 78(2): 714740,
2017. (preprint http://cadair.aber.ac.uk/dspace/handle/2160/43623) 

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) 

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) 

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. 

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. 

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): 3750,
2011. 

T. Friedrich, J. He , N. Hebbinghaus, F.
Neumann and C. Witt. Approximating Covering Problems by Randomized Search
Heuristics using MultiObjective Models. Evolutionary
Computation 18(4): 617633, 2010.


T. Chen, J. He, G. Chen and X. Yao.
Choosing selection pressure for widegap problems.
Theoretical Computer Science 411(6): 926934,2010. 

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. 

T. Chen, J. He, G. Sun, G. Chen, and
X. Yao, A
New Approach for Analyzing Average Time Complexity of Populationbased
Evolutionary Algorithms on Unimodal Problems. IEEE Transactions
on Systems, Man and Cybernetics, Part B 39 (5):
10921106, 2009. 

J. He, and L.
Kang. A
Mixed Strategy of Combining Evolutionary Algorithms with Multigrid
Methods. International Journal of Computer Mathematics
86(5): 837849, 2009. 

Y. Zhou, J. He, and Q. Nie. A Comparative Runtime Analysis of Heuristic Algorithms
for Satisfiability Problems. Artificial Intelligence
173(2): 240257, 2009. 

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): 319, 2009. 

S. Powers and J. He. A
Hybrid Artificial Immune System and Self Organising Map for Network
Intrusion Detection. Information Sciences
178(15): 30243042, 2008. 

A. Bennett, R. Johnston, E. Turpin and
J. He, Analysis of an Immune Algorithm for Protein Structure
Prediction. Informatica 32 (3): 245251,
2008.


J. He, C.
Reeves, C. Witt and X. Yao. A
Note on Problem Difficulty Measures in BlackBox Optimization:
Classification, Existence and Predictability. Evolutionary
Computation 15 (4): 435443, 2007 

Y. Zhou and J. He. A
Runtime analysis of evolutionary algorithms for constrained
optimization problems. IEEE Transactions on Evolutionary
Computation 11(5): 608619, 2007. 

Y. Zhou and J. He. Convergence analysis
of a selfadaptive multiobjective evolutionary algorithm based on
grids. Information Processing Letters
104(4): 117122, 2007. 

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):
281293, 2007. 

H. Dong, J. He, H. Huang and W. Hou.
Evolutionary programming using a mixed mutation
strategy. Information Sciences 177 (1):
312327, 2007.


X. Yao, Y. Liu, J. Li, J. He, and C. Frayn. Current developments and future directions of
bioinspired computation and implications for ecoinformatics.
Ecological Informatics 1 (1): 922, 2006.


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.


J. He and X. Yao. A Study of Drift Analysis for Estimating Computation
Time of Evolutionary Algorithms. Natural Computing 3
(1): 2135, 2004. 

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): 450458, 2004. 

J. He and X.
Yao. Towards an Analytic Framework for Analysing the
Computation Time of Evolutionary Algorithms. Artificial
Intelligence 145 (12): 5997, 2003. 

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): 143154,
2003. 

J. He and X.
Yao. From
an Individual to a Population: An Analysis of the First Hitting Time of
Populationbased Evolutionary Algorithms. IEEE Transactions on
Evolutionary Computation 6(5): 495511, 2002.


J. He and X.
Yu. Conditions for the convergence of evolutionary
algorithms. Journal of Systems Architecture 47 (7):
601612, 2001. 

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): 201250, 2001. 

J. He and X. Yao. Drift Analysis and Average Time Complexity of
Evolutionary Algorithms Artificial Intelligence 127
(1):5785, 2001.
(Erratum in Artificial
Intelligence, 140 (1): 245200, 2002). 

J. He, X. Yao and L. Kang. Drift Conditions for Time Complexity of Evolutionary
Algorithms. Journal of Software 12 (12): 17751783, 2001.


J. He, J. Xu,
and X. Yao. Solving
equations by hybrid evolutionary computation techniques. IEEE
Transactions on Evolutionary Computation 4 (3): 295304,
2000. 

J. He and L.
Kang. On the convergence rate of genetic algorithms.
Theoretical Computer Science 229 (12):2339, 1999. 

J. He, L. Kang
and Y. Chen. Multiple
structure computational model and its application in optimization.
Wuhan University Journal of Natural Sciences
1 (34): 593598, 1996. 

J. He, L. Kang
and Y. Chen Convergence of Genetic Evolution Algorithms for
Optimization. Parallel Algorithms and Applications 5
(1): 3756, 1995. 

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