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' 'NSGA-III' 'MO-CMA-ES' The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) and 'WFG4'-'WFG9' from Huband, et al. (2005) .


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0.6.2 by Dani Irawan, a year ago

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Authors: Dani Irawan [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports reticulate, nsga2R, lhs, nnet, stringr, randtoolbox, e1071, MASS, gtools, stats, utils, pracma

Suggests testthat

System requirements: Python 3.x with following modules: PyGMO, NumPy, and cloudpickle

See at CRAN