Professor Qingfu Zhang

 

School of Computer Science & Electronic Engineering,

University of Essex,

Wivenhoe Park,  Colchester,

CO4 3SQ, UK

E-mail:  qzhang (non-essex users should add @essex.ac.uk )

Tel No:  (44)  1206 872336 (work)

My office room no and photo can be found in  

My Departmental Homepage

 

Call for Paper: Special Session on Evolutionary Algorithms with Statistical & Machine Learning, WCCI 2010,  Barcelona,  18-23/July, 2010.

 

Research Areas: Neural Networks, Evolutionary Computation, Mathematical Programming, Statistical Data Analysis and Telecommunication Networks. 

 Publication  (downloadable and with codes )

PhD Students and Visitors

 Research Projects at Essex

 Editorships and Other External Activities

Short CV

 Links

 Web of Probability Model Based Heuristics (out of date, I will update it if I have time in the future) 

Web for CEC09 MOEA competition (Test Problems, Algorithms and Codes). New!

Patents

Awards:

§          The IEEE Transactions on Evolutionary Computation Outstanding Paper Award for

Q. Zhang and  H. Li,  MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition, IEEE Trans. on Evolutionary Computation, vol.11,  no. 6, pp712-731  2007.

§        The Winner of the Unconstrained Multiobjective Evolutionary Algorithm Competition in the Congress of Evolutionary Computation 2009.    

       The winning algorithm is MOEA/D.   

Recent Technical Reports:   

§        Y. Wang,   Z.  Cai,  and Q. Zhang,   Enhancing the Exploration Ability of Differential Evolution Through Orthogonal Crossover,  paper (pdf),   MATLAB code.  2010.

§        Zhou, S. Zhao, P. N. Suganthan, W. Liu and S. Tiwari,  Multiobjective Optimization Test Instances for the CEC 2009 Special Session and Competition, Working Report CES-487,  School of CS & EE, University of Essex, 11/2008.  paper(pdf)  and  MATLAB & C++ codes for the test instances

§        Q. Zhang,  W. Liu,  and H Li, The Performance of a New Version of MOEA/D on CEC09 Unconstrained MOP Test Instances, paper (pdf) and source code, Working Report CES-491, School of CS & EE, University of Essex, 02/2009.   This algorithm has been ranked first among 13 entries in the unconstrained MOEA competition in CEC 2009.  web for CEC09 MOEA competition.

 

Recent Journal Papers:

§        J. Sun, Q. Zhang and J. Li, Two-Level Evolutionary Approach to the Survivable Mesh-Based Transport Network Topological Design, Journal of Heuristics, Accepted, 2010.

§        Q. Zhang,   W. Liu,   E. Tsang and B. Virginas, Expensive Multiobjective Optimization by MOEA/D with Gaussian Process Model,  paper (pdf) and source code.   IEEE Trans on Evolutionary Computation,  2010.  

§        A. Zhou, Q. Zhang  and Y. Jin, Approximating the Set of Pareto Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm, paper(pdf),  C++ code,   IEEE Trans on Evolutionary Computation,   vol. 13,  no. 5,  pp1167-1189, 2009.  

§         H. Li and Q. Zhang,  Multiobjective Optimization Problems with Complicated Pareto Sets,  MOEA/D and NSGA-II, IEEE Trans on Evolutionary Computation, vol. 12,  no 2,  pp 284-302, April/2009,   paper (pdf) and C++ code.  

§        Q. Zhang, A. Zhou and Y. Jin, RM-MEDA: A Regularity Model Based Multiobjective Estimation of Distribution Algorithm, IEEE Trans. on Evolutionary Computation, vol. 12, no. 1, pp 41-63,  2008 MATLAB code , C++ code, and Erratum to figure 20. 

§        Q. Zhang and  H. Li,  MOEA/D: A Multi-objective Evolutionary Algorithm Based on Decomposition, IEEE Trans. on Evolutionary Computation, vol.11, no. 6, pp712-731  2007.    Results.    C++Code:  continuous MOP and knapsack problem.  Matlab Code. Java Code (written by Wudong Liu).

 
Brief Introduction to My Research: 
§        Multiobjective Test Problems  papers and codes (21 instances)  New!
§        Evolutionary Algorithms  for Multiobjective Optimization based on Decomposition (papers and codes New!)
§        Multiobjective Evolutionary Algorithm based on Regularity (paper and code New! )
§        Theory of Estimation of Distribution Algorithm
§        Our Working Experience on Use of Evolutionary Algorithms
§        Guided Mutation: Estimation of Distribution algorithm+GA (paper and codeNew! ).
§        Evolutionary Algorithm + Experimental Design
§        EDA+DE,  EDA+Two Different Local Search Techniques for Continuous Opt. Problems (paper and code).
§        Expensive Multiobjective Optimization (come soon)
§        Principal Component Analysis and Independent Component Analysis  (come soon)
§        Independent Component Analysis+UMDA
§        Linear Programming
§        Global Optimization
§        Combinatorial Optimization
§        EAs for Telecommunication networks.

 

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Last updated by Q. Zhang, 03/2009