Abstract
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.
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Li, J., Huang, S. (2007). Evolving in Extended Hamming Distance Space: Hierarchical Mutation Strategy and Local Learning Principle for EHW. In: Kang, L., Liu, Y., Zeng, S. (eds) Evolvable Systems: From Biology to Hardware. ICES 2007. Lecture Notes in Computer Science, vol 4684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74626-3_35
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DOI: https://doi.org/10.1007/978-3-540-74626-3_35
Publisher Name: Springer, Berlin, Heidelberg
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