Welcome to

Special Session on "Bridging Machine Learning and Evolutionary Computation"


       Machine learning is to discover patterns and knowledge from existing data, and predict future events. By nature, many machine learning problems can be modelled as optimization problems, often with more than one conflicting objectives such as accuracy and complexity. It is also common that these problems have many locally optimal solutions. Traditional local optimization methods may not work well. For these reasons, evolutionary algorithms (EAs) have been widely used as an optimization tool in the field of machine learning in recent years. On the other hand, ideas and techniques from machine learning can be used in and hybridized with EAs. A good example is estimation of distribution algorithms. It should also be a very promising research direction to study optimization problems from the machine learning point of view.
       This special session is intended to provide a forum for state-of-the-art research on the interdisciplinary work between EAs and machine learning. We solicit original contributions in, but not limited to, the following topics:

  • Evolutionary algorithms using machine learning techniques and their applications.
  • Machine learning algorithms using the ideas from Evolutionary algorithms.
  • Evolutionary clustering, feature extraction and feature selection.
  • Model selection by using evolutionary algorithms.
  • Empirical and/or theoretical comparisons between evolutionary single objective and multiobjective machine learning techniques.
  • New developments of EA techniques specialized for machine learning problems.
  • Ensemble Machine Learning Methods based on evolutionary algorithms.
  • Evolutionary multiobjective optimization techniques in machine learning.
  • Applications of EAs and machine learning on bioinformatics, computational biology and other areas.

       Professor Qingfu Zhang is the vice chair of task force on evolutionary algorithms based on probabilistic models, which aims to use machine learning techniques in evolutionary algorithms to improve EA's performance. The research interests of Dr. Chen include using functional analysis, a generalization of probabilistic models, in machine learning and using EAs to improve the performance of machine learning algorithms. This special session will benefit from this complementary organization. In addition, this research area has attracted much attention in both machine learning and evolutionary communities. For example, Professor Qingfu Zhang has successfully organized a special issue on "Bridging Machine Learning and Evolutionary Computation" in Neurocomputing, which received a significant submission.


Huanhuan Chen

       Huanhuan Chen received the B.Sc. degree from the University of Science and Technology of China, Hefei, China, in 2004, and Ph.D. degree, sponsored by Dorothy Hodgkin Postgraduate Award (DHPA), in computer science at the University of Birmingham, Birmingham, UK, in 2008. He is a professor with UBRI, School of Computer Science, University of Science and Technology of China. His research interests include machine learning, data mining and evolutionary computation. His PhD thesis on ensemble learning "Diversity and Regularization in Neural Network Ensembles" has received 2011 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award (the only winner), 2009 CPHC/British Computer Society Distinguished Dissertations Award (the runner up), IEEE Transactions on Neural Networks Outstanding 2009 Paper Award (bestowed in 2011), and International Neural Network Society(INNS) Young Investigator Award in 2015.

Aimin Zhou

       Aimin Zhou received the B.Sc. and M.Sc. degrees in computer science from Wuhan University, Wuhan, China, in 2001 and 2003, respectively, and the Ph.D. degree in computer science from the University of Essex, Colchester, U.K., in 2009. He is currently an Associate Professor with the Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, and the Department of Computer Science and Technology, East China Normal University, Shanghai, China. His main research areas are evolutionary computation, multiobjective optimization, metaheuristics, and their applications to image processing.

Qingfu Zhang

       Qingfu Zhang received the B.Sc. degree in mathematics from Shanxi University, China, in 1984, and the M.Sc. degree in applied mathematics and the Ph.D. degree in information engineering both from Xidian University, Shaanxi, China, in 1991 and 1994, respectively. He is currently a Professor with the Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, and a Professor on leave from the School of Computer Science and Electronic Engineering, University of Essex, Colchester U.K. His current research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Systems, Man, and Cybernetics Part B. He is also an Editorial Board Member of three other international journals. MOEA/D, a multiobjective optimization algorithm developed in his group, won the Unconstrained Multiobjective Optimization Algorithm Competition at the Congress of Evolutionary Computation in 2009, and he was a recipient of the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award.