Abstract
In recent years numerous evolutionary algorithms have been proposed to optimize multi–modal problems. These algorithms test the performance by benchmark functions for simulating real-world problems. However, the benchmark functions don’t have enough similarity and complexity compared to real world. Thus, Recurrent Benchmark Generator (RBG) is proposed in this paper to generate complex and different benchmark functions. This generator obtains a mass of modals by recurrent neural network, which are added various fluctuations of normal benchmark functions to keep a balance between complexity and gradient. The experimental results indicate that the novel approach produces more complex benchmark functions which are more conformed to real world problems.
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Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant No. 61572230, No. 61573166, No. 61373054, No. 61472164, No. 61472163, No. 61672262, No. 61640218, Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025, ZR2014JL042. Science and technology project of Shandong Province under Grant No. 2015GGX101025, Project of Shandong Province Higher Educational Science and Technology Program under Grant no. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12, No. 2016GGX101001.
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Sun, F., Wang, L., Yang, B., Zhou, J., Chen, Z. (2017). A Novel Method for Generating Benchmark Functions Using Recurrent Neural Network. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_68
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DOI: https://doi.org/10.1007/978-3-319-63309-1_68
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