Unconstrained gene expression programming

  • Authors:

  • Jianwei Zhang;Zhijian Wu;Zongyue Wang;Jinglei Guo;Zhangcan Huang

  • Affiliations:

  • State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, China;School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China and Computer Engineering College, Jimei University, Xiamen, Fujian, China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, China;School of Science, Wuhan University of Technology, Wuhan, Hubei, China

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

Many linear structured genetic programming are proposed in the past years. Gene expression programming, as a classic linear represented genetic programming, is powerful in solving problems of data mining and knowledge discovery. Constrains of gene expression programming like head-tail mechanism do contribution to the legality of chromosome. however, they impair the flexibility and adaptability of chromosome to some extend. Inspired by the diversity of chromosome arrangements in biology, an unconstrained encoded gene expression programming is proposed to overcome above constraints. In this way, the search space is enlarged; meanwhile the parallelism and the adaptability are enhanced. A group of regression and classification experiments also show that unconstrained gene expression programming performs better than classic gene expression programming.