Evolutionary Computation and Metaheuristic

Evolutionary Computation and Metaheuristic

Our major research focus is to combine ideas and techniques from evolutionary computation, traditional mathematical programming, and machine learning for designing efficient metaheuristic algorithms for dealing with hard search and optimization problems in the fields ranging from engineering design to e-commence and planning. The metaheuristic optimization group has an excellent research track record. The multi-objective evolutionary optimization algorithms based on decomposition (MOEA/D) developed by us have become one of the most widely used algorithmic frameworks in evolutionary computation.



  • Theory and algorithms
    • Metaheuristics including population and model based search techniques
    • Multiobjective optimization
    • New methodologies for balancing optimality and simplicity in multicriterion decision making
    • Online optimization algorithms
    • Automatic algorithm design
    • Neural computation
  • Applications
    • Engineering design
    • 5G network design
    • Robotics
    • Intelligent control
    • Transportation, scheduling, and planning
    • Molecular genetics and systems biology

Research Centres

  • The Metaheuristic Optimization Group
  • Algorithmic Information theory and Artificial Intelligence Lab