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Hybrid evolutionary programming using adaptive Levy mutation and modified Nelder-Mead method
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DOI：10.1007/s00500-018-3422-4.png)
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Abstract
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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 Levy mutation, in which the shape parameter of Levy 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.
Keywords:
Global optimization
Evolutionary programming
Fitness landscape
Levy distribution
Nelder-Mead method
Algorithm design
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