Background and research

I come from China. I got my Bachelor in Computer Engineering at Chinese Geoscience Univeristy in 1999. Afterwards I was a software engineer in Hainan Airline Company, China for 5 years. In 2004 I went to Dalana university of Sweden for Master's study in Artificial Intelligent. And now, I am a PhD student at the Department of Computer and Information Science in Norwegian University of Science and Technology.

My research interests are in evolution, coevolution, artificial neural network, neuroevolution and optimal search. Currently, my research focuses on the design and optimization of artificial neural network using coevolutionary algorithms. Reinforcement learning is a main task for our artificial nerual network.

Publications

1. Min Shi, Haifeng Wu and Hasan Fleyeh (2008). Support vector machines for traffic signs recognition, Proceedings of the International Joint Conference on Neural Networks 2008, Hongkong, China, pp 3820-3827. PDF

2. Min Shi, Haifeng Wu and Hasan Fleyeh (2008). A Robust Model for Traffic Signs Recognition Based on Support Vector Machines, Proceedings of the International Congress on Image and Signal Processing 2008, Sanya, China, pp 516-524. PDF

3. Haifeng Wu, Sohlberg Bjorn and Min Shi (2008). Single Tank System Identification Based on Neural Networks, Proceedings of the 8th Portuguese Conference on Automatic Control, Vila Real, Portugal, pp 175-179. PDF

4. Min Shi (2008). An Empirical Comparison of Evolution and Coevolution for Designing Artificial Neural Network Game Players, Proceedings of Genetic and Evolutionary Computation Conference 2008, Atlanta, USA, pp 379-386. [Best Paper Award Winner]. PDF

5. Min Shi and Haifeng Wu (2008). Evolving Efficient Connection for the Design of Artificial Neural Networks, Proceedings of the 18th International Conference on Artificial Neural Networks, Prague, Czech, pp 909-918. PDF

6. Min Shi and Boye Annfelt Høverstad (2009). PEEC: Evolving Efficient Connections Using Pareto Optimality, Proceedings of IEEE Congress on Evolutionary Computatio 2008, Trondheim, Norway, pp 1578-1584. PDF

7. Boye Annfelt Høverstad, Haaken Annfelt Moe and Min Shi (2009). Entropy and Mutual Information can Improve Fitness Evaluation in Coevolution of Neural Networks, Proceedings of IEEE Congress on Evolutionary Computatio 2008, Trondheim, Norway, pp 3199-3206. PDF

8. Min Shi and Haifeng Wu (2009). Pareto Cooperative Coevolutionary Genetic Algorithm Using Reference Sharing Collaboration, Proceedings of Genetic and Evolutionary Computation Conference 2009, Montreal, Canada, pp 867-874. PDF

9. Min Shi (2009). Comparison of Sorting Algorithms for Multi-fitness Measurement of Cooperative Coevolution, Proceedings of Genetic and Evolutionary Computation Conference 2009, Montreal, Canada, pp 2583-2588. PDF

10. Min Shi (2011). Empirical Analysis of Cooperative Coevolution using Blind Decomposition, Proceedings of Genetic and Evolutionary Computation Conference 2011, Dublin, Ireland, pp 141-142. PDF

11. Min Shi (2011). Natural Vs. Unnatural Decomposition in Cooperative Coevolution, Proceedings of International Conference on Intelligent Computing 2011, Zhengzhou, China, in press. PDF