A new adaptive crossover operator for the preservation of useful schemata

  • Authors:

  • Fan Li;Qi-He Liu;Fan Min;Guo-Wei Yang

  • Affiliations:

  • College of computer science and engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China;College of computer science and engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China;College of computer science and engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China;College of computer science and engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China

  • Venue:
  • ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
  • Year:
  • 2005

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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.