ISSN 1002-1027  CN 11-2952/G2

Acta scientiarum naturalium Universitatis Pekinensis

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Flow-Shop Scheduling Problem Based Improved Adaptive Genetic Algorithms

CHI Bin1, YE Qingkai1, XING Fei2   

  1. 1Department of Mechanics and Engineering Science, Peking University, Beijing, 100871, E-mail: chibin@mech.pku.edu.cn; 2Department of Mathematics, Inner Mongolia University, Hohhot, 010021
  • Received:2002-05-14 Online:2003-05-20 Published:2003-05-20

Abstract: Two kinds of improved adaptive genetic algorithms and encoding & decoding methods are presented. The experiment data shows that the improved adaptive genetic algorithms are superior to the pure genetic algorithms and other adaptive genetic algorithms in qualify of solution and efficiency.