My main research interests include:
MOEA/D: Population Based Method+ Aggregation Methods for Multiobjective Optimization
RM-MEDA: Using regularity property to design efficient multiobjective estimation of distribution algorithm
Multiobjective Optimization Test Problems with Many Objectives and Complicated Pareto Set
Expensive Optimization
Estimation of Distribution Algorithm + Genetic Algorithm: Guided Mutation
Evolutionary Algorithm + Experimental Design: Orthogonal Crossover
Different Evolution
Theory of Estimation of Distribution Algorithm
Principal Component Analysis and Independent Component Analysis
EAs for Telecommunication Networks
Linear Programming
Some of my funded projects:
Network Design by Multi-Objective Optimisation (BT short-term fellowship), 2007.
Modelling Distribution of Pareto-optimal Solutions for Multi-Objective Optimisation (Funded by Honda Research Institute Europe, 2004– )
Researchers at Essex: Qingfu Zhang, Edward Tsang, and Aimin Zhou (project PhD student)
Researchers at Honda: Yaochu Jin and Bernhard Sendhoff.
Market-based Workforce Management (Funded by BT, 2005– )
Researchers at Essex: Edward Tsang, Qingfu Zhang, Tim Gosling, Wudong Liu (project PhD student).
Researchers at BT: Botond Virginas (BT), Chris Voudouris (BT & Essex) and G Owusu (BT)
Advanced Evolutionary Methods for Learning Finite State Machines (Funded by RPF of Essex University, 2006)
Researchers: S. Lucas, Qingfu Zhang, R. Poli, Hui Li (Research Assistant), A. Moraglio (Research Assistant).
Estimation of Distribution Algorithm (Funded by EPSRC, 2002–2003)
Researchers: Qingfu Zhang, Edward Tsang and John Ford and Jianyong Sun (Project RA)
Heuristics for Routing in Telecommunication (Funded by Royal Academy of Engineering, 2003)
Researchers: Qingfu Zhang, Gaoxi Xiao (Academic visitor from Nanyang Technological University, Singapore), Jianyong Sun, and Edward Tsang.
Hybrid Optimization Methods (Funded by University of Essex, 2001–2002)
Researchers: Qingfu Zhang, Edward Tsang, John Ford, Hui Li and Tim Gosling
Studentships may be available for strong and appropriate applicants from time to time.