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He, Jun


Research Activities

Research Group

Research Interests

  • Evolutionary Algorithms and Global Optimization
    1. Analysis of evolutionary algorithms
    2. Design of evolutionary algorithms
    3. Applications of evolutionary algorithms
  • Evolutionary Computation and Complex Systems Modelling
  • Computational Intelligence and Cyber Security
    1. Artificial immune system and self organizing map for network intrusion detection
  • Computational Intelligence and Data Analysis

Research Work

  1. Introduced drift analysis to the theoretical analysis of evolutionary algorithms [2001AI, 2004NC]. This approach has become a popular tool for estimating the computation time of evolutionary algorithms.
    Review on this work
    "Expected runtime analysis inspects the average runtime of an algorithm on a particular problem, and can exploit mature probabilistic techniques, such as drift analysis and others .'' Z Wu, M Kolonko, R Moehring (2017)
    "drift analysis, a method that provided important insights into the computational complexity of discrete EAs over the last decade.'' Agapie, Agapie, Rudolph and Zbaganu (2013)
    ``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.'' Doerr, Johannsen and Winzen (2012)
    ``Drift analysis is a powerful tool used to bound the optimization time of evolutionary algorithms (EAs)." "Recently drift analysis, a technique that goes back to the 1940s, was introduced for the analysis of EAs by He and Yao.'' Oliveto and Witt (2011).
    ``Drift analysis, which is widely used by now, has been put forward by He and Yao.'' Jagersküpper (2011).
  2. Proposed population scalability/speedup [2002TEC,2016TEC] for analyzing population-based evolutionary algorithms.
  3. Analyzed the convergence rate of evolutionary algorithms [1999TCS, 2016TEC].
  4. Understood the problem difficulty in evolutionary computation [2007EC, 2015TEC].
  5. Proposed mixed strategy evolutionary algorithms [2007IS], which were inspired from game theory
  6. Solving linear equations and partial differential equations using evolutionary computation techniques [1999TEC]
  7. Applied artificial immune system and self organizing map for network intrusion detection [2008IS]

EPSRC Grants