Abstract
This research deals with the comparison of three novelty approaches for constant estimation in analytic programming (AP) powered by Success-history based Differential evolution (SHADE). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. This paper offers another strategy to already known and used by the AP from the very beginning and approaches published recently in 2016. This paper compares these procedures and the discussion also includes nonlinear fitting and metaevolutionary approach. As the main evolutionary algorithm, a differential algorithm in the version SHADE for the main process of AP is used. The proposed comparison is performed out on quintic, sextic, Sine 3 and Sine 4 benchmark problems.
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Acknowledgement
This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further it was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S and by Internal Grant Agency of Tomas Bata University in Zlin under the project No. IGA/CebiaTech/2017/004.
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Oplatkova, Z.K., Viktorin, A., Senkerik, R. (2019). Comparison of Three Novelty Approaches to Constants (Ks) Handling in Analytic Programming Powered by SHADE. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_12
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DOI: https://doi.org/10.1007/978-3-319-97888-8_12
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