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A New Adaptive Crossover Operator for the Preservation of Useful Schemata

  • Fan Li
  • Qi-He Liu
  • Fan Min
  • Guo-Wei Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)

Abstract

In genetic algorithms, commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained in the evolution because of their random choices of crossover points. To overcome this defect, a new adaptive crossover operator based on the Rough Set theory is proposed in this paper. By using this specialized crossover operator, useful schemata can be found and have a higher probability of surviving recombination regardless of their defining length. We compare the proposed crossover operator’s performance with the two-point crossover operator on several typical function optimization problems. The experiment results show that the proposed crossover operator is more efficient.

Keywords

Crossover Operator Crossover Point Decision Table Promising Performance Replacement Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fan Li
    • 1
  • Qi-He Liu
    • 1
  • Fan Min
    • 1
  • Guo-Wei Yang
    • 1
  1. 1.College of computer science and engineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China

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