Xin Li   Research Assistant, Ph.D. Candidate

Artificial Intelligence Laboratory
Department of Computer Science
University of Illinois at Chicago

Office:          Room 3028 ERF, 842 W. Taylor St., Chicago, IL 60607
Telephone:   (312) 413-4075
Email:           xli6[at]uic[dot]edu,  xli1[at]cs[dot]uic[dot]edu



   Ph.D. Candidate in Computer Science (Sep.2001–Present)
      Department of Computer Science, University of Illinois at Chicago

   B.E. in Computer Science (Sep.1997–Jul.2001)
      College of Computer Science and Technology, Huazhong University of Science & Technology, China


  Research Interests 

My Research interests mainly include Evolutionary Computation, Genetic Algorithms, Genetic Programming, Gene Expression Programming, Machine Learning, Data Mining, Knowledge Discovery and Management.


  Project Description 

   Data mining for manufacturing and design processes
      Manufacturing and design processes often generate large scale data sets with lots of numeric and nominal attributes. Discovering and predicting the hidden pattern or relationships among all of these data attributes is pivotal in figuring out the crucial factors that affect the manufacturing process and assisting the improvement of the production quality accordingly. This ongoing project specifically targets at this issue by building up a robust data mining system which can efficiently deal with high dimensional large data sets. The techniques we have investigated include decision trees, decision rules, neural networks and some other traditional machine learning methods. Our current interests focus on applying some adaptive evolutionary computational methods to solve complex problems or evolve complex systems. One of the promising approaches we stick to is using Gene Expression Programming (GEP) algorithm. Belonging to the family of Genetic Algorithms (GAs), GEP is a recently-developed evolutionary algorithm that is capable of evolving computer programs and predicts mathematical functions from experimental data. Because of its linear chromosome representation and its separation of the solution and search space, GEP dramatically improves upon traditional genetic programming with respect to complexity and time efficiency, and can solve various types of modeling and optimization problems. Our experiments conducted on multi-category pattern classification problems have demonstrated the capability of GEP to mine accurate but more compact classification rules, compared to traditional machine learning algorithms. Further efforts include adding incremental learning features to the algorithm for its better performance and applying our data mining tools to more practical manufacturing problems.


  Selected Publications 

   Xin Li, Chi Zhou, Weimin Xiao, and Peter C. Nelson. "Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures." In Proceedings of the 4th International Conference on Machine Learning and Applications (ICMLA'05), pp. 337-342. Dec 15-17, 2005. Los Angeles, CA, USA. (available here in PDF)

   Xin Li. "Self-Emergence of Structures in Gene Expression Programming." In AAAI/SIGART Doctoral Consortium 2005. July 9-10, 2005. Pittsburgh, Pennsylvania, USA. (available here in PDF)

   Xin Li, Chi Zhou, Weimin Xiao, and Peter C. Nelson. "Prefix Gene Expression Programming." In Late Breaking Paper at Genetic and Evolutionary Computation Conference (GECCO-2005). June 25-29,2005. Washington, D.C., USA. (available here in PDF)

   Zhuli Xie, Xin Li, Barbara Di Eugenio, Weimin Xiao, Thomas M. Tirpak, and Peter C. Nelson. "Using Gene Expression Programming to Construct Sentence Ranking Functions for Text Summarization." In Proceedings of the 20th International Conference on Computational Linguistics (COLING-2004). August 23-27, 2004. Geneva, Switzerland.  (available here in PDF)

   Xin Li, Chi Zhou, Peter C. Nelson, and Thomas M. Tirpak. "Investigation of Constant Creation Techniques in the Context of Gene Expression Programming." In Late Breaking Paper at Genetic and Evolutionary Computation Conference (GECCO-2004). June 26-30, 2004. Seattle, Washington, USA. (available here in PDF)


Copyright©Xin Li 2002-2006. All rights reserved.