Research

Participated in research of the following areas: evolutionary computation, computational optimization, machine learning, data analysis, numerical analysis, parallel algorithms.

Research Interest

• • Theoretical analysis of evolutionary algorithms and other randomised search heuristics

  • Multi-objective evolutionary algorithms for constrained optimization

  • Coevolutionary system, game strategy and artificial life

  • Applications of machine learning in cyber-security, e.g. networks intrusion detection

  • Applications of machine learning in data analysis, e.g. electronic health records

  • Applications of artificial intelligence in language education

Research Highlight

• 1. 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.''

     • "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.''

     • "drift analysis, a standard tool in theory of randomised search heuristics."

     • "Drift analysis is one of the major tools for analysing evolutionary algorithms and nature-inspired search heuristics."

  2. Designed helper and equivalent objective differential evolution for constrained optimization (HECO-DE)

     • HECO-DE was ranked 1st in 2019 in IEEE CEC Competition on Constrained Real Parameter Optimization.

  3. 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)."

  4. 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"

Research Grant

• • 2011-2015 Principal Investigator: Evolutionary Approximation Algorithms for Optimization: Algorithm Design and Complexity Analysis. EPSRC (£331K).

  • 2005-2008 Researcher Co-investigator: Computational Complexity Analysis of Evolutionary Algorithms. EPSRC (£291K).