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Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems
Abstract
We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE. We present an algorithm-a new version of the DE algorithm-for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems. The results show that our algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained.
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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
- Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2006-12
- Volume
- 10
- Issue
- 6
- Pages
- 646-657
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2162145193
- Cited by
- 3,077
- References
- 1
- FWCI
- 48.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2006.872133
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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