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Experimental Analysis of Selective Imitation for Multifactorial Differential Evolution

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

Abstract

Recently, evolutionary multitasking optimization (EMTO) is proposed as a new emerging optimization paradigm to simultaneously solve multiple optimization tasks in a cooperative manner. In EMTO, the knowledge transfer between tasks is mainly carried out through the assortative mating and selective imitation operators. However, in the literature of EMTO, little study on the selective imitation operator has yet been done to provide a deeper insight in the knowledge transfer across different tasks. Based on this consideration, we firstly study the influence of the inheritance probability (IP) of the selective imitation on an EMTO algorithm, multifactorial differential evolution (MFDE), through the experimental analysis. Then, an adaptive inheritance mechanism (AIM) is introduced into the selective imitation operator of MFDE to automatically adjust the IP value for different tasks at different evolutionary stages. The experimental results on a suite of single-objective multitasking benchmark problems have demonstrated the effectiveness of AIM in enhancing the performance of MFDE.

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Notes

  1. 1.

    The results of the preliminary experimental studies on IP will be shown in Sect. 4.2.

References

  1. Bali, K.K., Gupta, A., Feng, L., Ong, Y.S., Siew, T.P.: Linearized domain adaptation in evolutionary multitasking. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1295–1302. IEEE (2017)

    Google Scholar 

  2. Bali, K.K., Ong, Y.S., Gupta, A., Tan, P.S.: Multifactorial evolutionary algorithm with online transfer parameter estimation: MFEA-II. IEEE Trans. Evol. Comput. 24, 69–83 (2019)

    Article  Google Scholar 

  3. Da, B., et al.: Evolutionary multitasking for single-objective continuous optimization: benchmark problems, performance metrics and baseline results. Technical report, Nanyang Technological University (2016)

    Google Scholar 

  4. Feng, L., et al.: An empirical study of multifactorial PSO and multifactorial DE. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 921–928. IEEE (2017)

    Google Scholar 

  5. García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10), 959–977 (2009)

    Article  Google Scholar 

  6. Gupta, A., Ong, Y.S., Feng, L.: Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2015)

    Article  Google Scholar 

  7. Ong, Y.-S.: Towards evolutionary multitasking: a new paradigm in evolutionary computation. In: Senthilkumar, M., Ramasamy, V., Sheen, S., Veeramani, C., Bonato, A., Batten, L. (eds.) Computational Intelligence, Cyber Security and Computational Models. AISC, vol. 412, pp. 25–26. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0251-9_3

    Chapter  Google Scholar 

  8. Tang, J., Chen, Y., Deng, Z., Xiang, Y., Joy, C.P.: A group-based approach to improve multifactorial evolutionary algorithm. In: IJCAI, pp. 3870–3876 (2018)

    Google Scholar 

  9. Wen, Y.W., Ting, C.K.: Parting ways and reallocating resources in evolutionary multitasking. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 2404–2411. IEEE (2017)

    Google Scholar 

  10. Zheng, X., Lei, Y., Gong, M., Tang, Z.: Multifactorial brain storm optimization algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds.) BIC-TA 2016. CCIS, vol. 682, pp. 47–53. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-3614-9_6

    Chapter  Google Scholar 

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Acknowledgments

This work was supported in part by the Natural Science Foundation of Fujian Province of China (2018J01091), the National Natural Science Foundation of China (61572204, 61502184), the Postgraduate Scientific Research Innovation Ability Training Plan Funding Projects of Huaqiao University (18014083013), and the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University. The authors would like to express their deep gratitude to Prof. Y. S. Ong from School of computer science and engineering, Nanyang technological university (NTU), Singapore, for his patient guidance, and the Data Science and Artificial Intelligence Research Center at NTU for the support to our work.

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Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Quanzhou, China

    Deming Peng, Yiqiao Cai, Shunkai Fu & Wei Luo

  2. Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen, 361000, China

    Deming Peng, Yiqiao Cai, Shunkai Fu & Wei Luo

Authors
  1. Deming Peng
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  2. Yiqiao Cai
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  3. Shunkai Fu
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  4. Wei Luo
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Corresponding author

Correspondence to Yiqiao Cai .

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Editors and Affiliations

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

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Peng, D., Cai, Y., Fu, S., Luo, W. (2020). Experimental Analysis of Selective Imitation for Multifactorial Differential Evolution. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_2

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  • DOI: https://doi.org/10.1007/978-981-15-3425-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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