MaOEA: Many Objective Evolutionary Algorithm

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 ORCID iD [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

Downloads:

Reference manual: MaOEA.pdf
Package source: MaOEA_0.5.2.tar.gz
Windows binaries: r-devel: MaOEA_0.5.2.zip, r-devel-gcc8: MaOEA_0.5.2.zip, r-release: MaOEA_0.5.2.zip, r-oldrel: MaOEA_0.5.2.zip
OS X binaries: r-release: MaOEA_0.5.2.tgz, r-oldrel: MaOEA_0.5.2.tgz

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