Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW

  • Jie Li
  • Shitan Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4684)


In this paper extended Hamming distance is introduced to construct the search space. According to the features of this space, a hierarchical mutation strategy is developed for the purpose of enlarging the search area with less computation effort. A local learning principle is proposed. This principle is used to ensure that no mutation operates on the same locus of chromosomes within one generation. An evaluation method called fitness effort for calculating computational effort per increased fitness value is also given. Experimental results show that the proposed hybrid approach of hierarchical mutation and local learning can achieve better performance than traditional methods.


Evolvable Hardware extended Hamming distance space hierarchical mutation local learning fitness effort 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jie Li
    • 1
  • Shitan Huang
    • 1
  1. 1.Xi’an Microelectronics Technology Institute, 710071 Xi’an, ShannxiChina

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