Dr. Jun He is an Associate Professor within the Department of Computer Science, School of Science and Technology. He is also the Module Leader of Machine Learning for Data Analytics for UG Year 2, Practical Machine Learning Methods for Data Mining and Work-Based Project for MSc students.
Dr. Jun He attended Wuhan University in China where he received his BSc and MSc degrees in Mathematics and was awarded his PhD degree in Computer Science. He has held previous positions in the United Kingdom at University of Birmingham and Aberystwyth University. In China, he has also held positions at Harbin Institute of Technology and Beijing Jiaotong University. He is also an honorary staff with the Department of Computer Science at Aberystwyth University. Further information is available at his personal homepage.
Dr. He is a member of the Computing and Informatics Research Centre and participates in research of the following areas: evolutionary computation, computational optimization, machine learning, data analysis, numerical analysis, parallel algorithms. His current research interests are
- Design and analysis of evolutionary algorithms and randomised search heuristics
- Applications of evolutionary algorithms and randomised search heuristics in optimization
- Coevolutionary system, game, search and artificial life, e.g., path planning in games
- Applications of machine learning in cyber-security, e.g., networks intrusion detection, IoT (Internet of Things) devices behaviour monitoring
- Applications of machine learning in data analysis, e.g., electronic health records
Opportunities arise to carry out postgraduate research towards an MPhil / PhD in the areas identified below. Fully-funded Nottingham Trent University PhD studentships are available. For more information please contact by email.
- Area 1: Machine learning using electronic health records to select high-risk patients
- Area 2: Deep learning for anomaly traffic detection in Internet of Things devices
Highlights of his research work:
- Introduced drift analysis to theoretical analysis of evolutionary algorithms. 'Drift analysis was introduced to the theory of evolutionary algorithms by He and Yao. It soon became one of the strongest tools both for proving run-time guarantees for many evolutionary algorithms and for giving evidence that some algorithms cannot solve certain problems.' 'drift analysis, a method that provided important insights into the computational complexity of discrete EAs over the last decade.' 'drift analysis, a standard tool in theory of randomised search heuristics'
- Designed helper and equivalent objective differential evolution for constrained optimization (HECO-DE), ranked 1st in 2019 in IEEE CEC Competition on Constrained Real Parameter Optimization.
- Proposed average convergence rate of evolutionary algorithms. 'The fourth type of convergence representation is the average convergence rate that specifically measures the rate of fitness change as proposed by He and Lin.' 'Convergence ratio can be evaluated by Markov chain analysis, however, this process is complicated from theoretical and practical point of view. For this reason, the convergence indicator is described as (He and Lin, 2016).'
- Rigorously analysed easiest and hardest functions to evolutionary algorithms. 'The theory indicates easy problems for one algorithm may be difficult for another, and vice versa.' 'This further explains RSHs (randomized search heuristics) are not always efficient for all problems.'
Outside of his work at the Nottingham Trent University, Dr. Jun He is a senior member of IEEE and a member of the IEEE Computational Intelligence Society and the British Computer Society.
- Journal of Optimization
- Applied Computational Intelligence and Soft Computing
Sponsors and collaborators
Dr Jun He has had his research sponsored primarily by grants received from the UK Engineering and Physical Sciences Research Council.
- 2011 to 2015. Principal Investigator in the UK EPSRC grant: "Evolutionary Approximation Algorithms for Optimization: Algorithm Design and Complexity Analysis" (£331K).
- 2005 to 2008. Researcher Co-investigator in the UK EPSRC grant : "Computational Complexity Analysis of Evolutionary Algorithms" (£291K).
Dr He has also been awarded several Research Fellowships from 2001 to 2005, listed below.
- 2004 to 2005. Research Fellow in the UK EPSRC grant : “Market Based Control of Complex Computational Systems" (£281K).
- 2003. Research Fellow in the UK EPSRC grant : “Adaptive Divide and Conquer --Nature's Way to Cope with Complexity" (£59K).
- 2001 to 2003. Research Fellow in the UK EPSRC grant : “Average Computation Time of Evolutionary for Combinatorial Optimization Problems" (£62K).
For more of his work, Dr. Jun He has articles in the following publications:
- Wang, C., Chen, Y., He, J., & Xie, C. (2021). Error analysis of elitist randomized search heuristics. Swarm and Evolutionary Computation.
- Chen. Y. & He, J. (2021). Average Convergence Rate of Evolutionary Algorithms in Continuous Optimization. Information Sciences.
- Xu, T., He, J., & Shang, C. (2020). Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization. IEEE Transactions on Cybernetics
- Chong, S. Y., Tiňo, P., & He, J. (2019). Coevolutionary systems and PageRank. Artificial Intelligence.
- Huang, W., Xu, T., Li, K., & He, J. (2019). Multiobjective differential evolution enhanced with principle component analysis for constrained optimization. Swarm and Evolutionary Computation
- 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.
- Pang, J., He, J., & Dong, H. (2018). Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method. Soft Computing.
- Zhou, Y., Xiang, Y., Chen, Z., He, J., & Wang, J. (2018). A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization Problems. IEEE Transactions on Cybernetics.
- Chong, S. Y., Tiňo, P., He, J., & Yao, X. (2018). A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks. Evolutionary Computation.
- 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.
- He, J., & Yao, X. (2017). Average drift analysis and population scalability. IEEE Transactions on Evolutionary Computation.
- 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,
- He, J., & Lin, G. (2016). Average convergence rate of evolutionary algorithms. IEEE Transactions on Evolutionary Computation.
- He, J., Chen, T., & Yao, X. (2015). On the easiest and hardest fitness functions. IEEE Transactions on Evolutionary Computation.
- 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.