The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Novel composition test functions for numerical global optimization
Abstract
In the evolutionary optimization field, there exist some algorithms taking advantage of the known property of the benchmark functions, such as local optima lying along the coordinate axes, global optimum having the same values for many variables and so on. Multiagent genetic algorithm (MAGA) is an example for this class of algorithms. In this paper, we identify shortcomings associated with the existing test functions. Novel hybrid benchmark functions, whose complexity and properties can be controlled easily, are introduced and several evolutionary algorithms are evaluated with the novel test functions.
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Citations
Cites
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003) · Evolutionary Computation · doi:10.1162/106365603321828970 · Cited by 2,601
Cited by
Hansen, N., Müller, S. D., & Koumoutsakos, P. (2003) · Evolutionary Computation · doi:10.1162/106365603321828970 · Cited by 2,601
Differential evolution algorithm with ensemble of parameters and mutation strategies
Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2010.04.024 · Cited by 1,362
Self-citation
Metadata
- Authors
- Liang, J. J., Suganthan, P. N., & Deb, K.
- Venue
- Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005.
- Publisher
- IEEE
- Published
- 2005-08-30
- Pages
- 68-75
- OpenAlex
- W2137514952
- Cited by
- 432
- References
- 2
- FWCI
- 5.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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