Abstract
This research paper presents an analysis of the population activity in Differential Evolution algorithm (DE) during the optimization process. A state-of-art DE variant – Success-History based Adaptive DE (SHADE) is used and the population activity is analyzed through Complex Network (CN) created from mutation, crossover and selection steps. The analysis is done on the CEC2015 benchmark set and possible future research directions for the population sizing are suggested.
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References
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Acknowledgements
This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.
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Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T. (2018). Towards Better Population Sizing for Differential Evolution Through Active Population Analysis with Complex Network. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_22
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DOI: https://doi.org/10.1007/978-3-319-61566-0_22
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