[aut,
cre]A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008> 'NSGA-III' <doi:10.1109/TEVC.2013.2281535> 'MO-CMA-ES' <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and 'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
| Version: | 0.5.2 |
| Imports: | reticulate, nsga2R, lhs, nnet, stringr, randtoolbox, e1071, MASS, gtools, stats, utils |
| Suggests: | testthat |
| Published: | 2019-10-28 |
| Author: | Dani Irawan [aut,
cre] |
| Maintainer: | Dani Irawan <irawan_dani at yahoo.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| SystemRequirements: | Python 3.x with following modules: PyGMO, NumPy, and cloudpickle |
| Citation: | MaOEA citation info |
| Materials: | README |
| CRAN checks: | MaOEA results |
| Reference manual: | MaOEA.pdf |
| Package source: | MaOEA_0.5.2.tar.gz |
| Windows binaries: | r-devel: MaOEA_0.5.2.zip, r-release: MaOEA_0.5.2.zip, r-oldrel: MaOEA_0.5.2.zip |
| macOS binaries: | r-release: MaOEA_0.5.2.tgz, r-oldrel: MaOEA_0.5.2.tgz |
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