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Drift analysis of mutation operations for biogeography-based optimization

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Abstract

As an essential factor of evolutionary algorithms (EAs), mutation operator plays an important role in exploring the search space, maintaining the diversity of individuals and breaking away local optimums. In most standard evolutionary algorithms, the mutation operator is independent from the recombination operator. Nevertheless, in biogeography-based optimization (BBO), the mutation operator is affected not only by predefined constants but also by recombination models, namely the migration operator. However to date, the relationship between the mutation and migration has never been investigated. To reveal the relationship and evaluate the mutation models, we utilize drift analysis to investigate the expected first hitting time of BBO with different migration models. The analysis compares three different kinds of mutation models in a mathematical way and the conclusion is helpful for designing migration models of BBO. The simulation results are also in agreement with our analysis.

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Notes

  1. There is a typo in (14) of Simon (2008). Simon shows the correct formula in http://academic.csuohio.edu/simond/bbo/.

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Acknowledgments

This work is sponsored by the National Natural Science Foundation of China under Grant No. 61203250, No. 61075064, No. 61034004 and No. 61005090, Program for New Century Excellent Talents in University of Ministry of Education of China (NCET-10-0633), Ph.D. Programs Foundation of Ministry of Education of China (20100072110038) and Singapore Academic Research Fund under grants R397000139133, R397000157112 and C397000043511. The authors would like to acknowledge Miss Amy Sun from the Johns Hopkins University for polishing the English writing. The author Weian Guo appreciates the China Scholarship Council (CSC) for supporting his living expenses in Singapore and thanks his wife Jiali Wang’s support!

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Correspondence to Weian Guo.

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Communicated by V. Loia.

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Guo, W., Wang, L., Ge, S.S. et al. Drift analysis of mutation operations for biogeography-based optimization. Soft Comput 19, 1881–1892 (2015). https://doi.org/10.1007/s00500-014-1370-1

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