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Dynamic Fitness Landscape Analysis on Differential Evolution Algorithm

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Bio-inspired Computing – Theories and Applications (BIC-TA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 682))

Abstract

Dynamic fitness landscape analyses mainly try to figure out the performance of evolutionary algorithms through some simple graphs and effective data. In this paper, we focus on one of evolutionary algorithms named as differential evolution (DE) algorithm. Six benchmark functions we selected because of different properties are involved in our experiments using metrics of dynamic fitness landscape analyses to test. According to experimental results, they shows obviously that differential evolution algorithm can calculate low dimension of benchmark functions and is very hard to handle high dimension. When a benchmark function becomes more and more complicate within higher dimension, sometimes differential evolution algorithm can get good results, but most of time there is no result at all. Dynamic fitness landscape analyses truly obtain experimental results and more details as differential evolution algorithm.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China with Grant No. 61573157, the Fund of Natural Science Foundation of Guangdong Province of China with Grant No. 2014A030313454 and the Key Project of Natural Statistical Science and Research with the Grant No. 2015LZ30, the National Natural Science Foundation of China with the Grant No. 61561024 and 61562038.

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Authors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangdong Province, 510642, People’s Republic of China

    Shuling Yang, Kangshun Li, Wei Li, Weiguang Chen & Yan Chen

  2. School of Information Engineering, Jiangxi University of Science and Technology, Jiangxi, 341000, People’s Republic of China

    Wei Li

Authors
  1. Shuling Yang
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  2. Kangshun Li
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  3. Wei Li
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  4. Weiguang Chen
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  5. Yan Chen
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Corresponding author

Correspondence to Kangshun Li .

Editor information

Editors and Affiliations

  1. Xidian University , Xi’an, China

    Maoguo Gong

  2. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  3. China University of Petroleum, Qingdao, China

    Tao Song

  4. Southwest Jiaotong University , Chengdu, China

    Gexiang Zhang

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© 2016 Springer Nature Singapore Pte Ltd.

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Yang, S., Li, K., Li, W., Chen, W., Chen, Y. (2016). Dynamic Fitness Landscape Analysis on Differential Evolution Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_23

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  • DOI: https://doi.org/10.1007/978-981-10-3614-9_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3613-2

  • Online ISBN: 978-981-10-3614-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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