The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Success-history based parameter adaptation for Differential Evolution
Abstract
Differential Evolution is a simple, but effective approach for numerical optimization. Since the search efficiency of DE depends significantly on its control parameter settings, there has been much recent work on developing self-adaptive mechanisms for DE. We propose a new, parameter adaptation technique for DE which uses a historical memory of successful control parameter settings to guide the selection of future control parameter values. The proposed method is evaluated by comparison on 28 problems from the CEC2013 benchmark set, as well as CEC2005 benchmarks and the set of 13 classical benchmark problems. The experimental results show that a DE using our success-history based parameter adaptation method is competitive with the state-of-the-art DE algorithms.
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Cites
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Metadata
- Authors
- Tanabe, R., & Fukunaga, A.
- Venue
- 2013 IEEE Congress on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 71-78
- OpenAlex
- W2144051602
- Cited by
- 1,367
- References
- 17
- FWCI
- 18.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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