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Self-adaptive Differential Evolution Algorithm for Numerical Optimization
Abstract
In this paper, we propose a novel self-adaptive differential evolution algorithm (SaDE), where the choice of learning strategy and the two control parameters F and CR are not required to be pre-specified. During evolution, the suitable learning strategy and parameter settings are gradually self-adapted according to the learning experience. The performance of the SaDE is reported on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization.
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Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
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Metadata
- Authors
- Qin, A. K., & Suganthan, P. N.
- Venue
- 2005 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2005-12-13
- Volume
- 2
- Pages
- 1785-1791
- OpenAlex
- W2129724855
- Cited by
- 1,215
- References
- 1
- FWCI
- 12.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Qin, A. K., & Suganthan, P. N. (2005). Self-adaptive Differential Evolution Algorithm for Numerical Optimization. 2005 IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/cec.2005.1554904
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