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Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method

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Abstract

Evolutionary programming has been widely applied to solve global optimization problems. Its performance is related to both mutation operators and fitness landscapes. In order to make evolutionary programming more efficient, its mutation operator should adapt to fitness landscapes. The paper presents novel hybrid evolutionary programming with adaptive Lévy mutation, in which the shape parameter of Lévy probability distribution adapts to the roughness of local fitness landscapes. Furthermore, a modified Nelder–Mead method is added to evolutionary programming for enhancing its exploitation ability. The proposed algorithm is tested on 39 selected benchmark functions and also benchmark functions in CEC2005 and CEC2017. The experimental results demonstrate that the overall performance of the proposed algorithm is better than other algorithms in terms of the solution accuracy.

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Acknowledgements

We would like to acknowledge the support from the National Science Foundation of China (No. 61472095) and EPSRC under Grant No. EP/I009809/1.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Jinwei Pang & Hongbin Dong

  2. School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK

    Jun He

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  1. Jinwei Pang
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  2. Jun He
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  3. Hongbin Dong
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Correspondence to Hongbin Dong.

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Pang, J., He, J. & Dong, H. Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead method. Soft Comput 23, 7913–7939 (2019). https://doi.org/10.1007/s00500-018-3422-4

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