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Differential Evolution (DE) stochastic algorithms for global optimization of problems with and without constraints. The aim is to curate a collection of its state-of-the-art variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it only provides an implementation of the 'jDE' algorithm by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.

Type Size Name Uploaded Downloads Labels
conda 39.3 kB | linux-64/r-deoptimr-1.0_6-r3.4.1_0.tar.bz2  7 years and 11 months ago 710 main cf201901
conda 39.2 kB | osx-64/r-deoptimr-1.0_6-r3.4.1_0.tar.bz2  7 years and 11 months ago 4132 main cf201901
conda 35.7 kB | linux-64/r-deoptimr-1.0_6-r3.3.2_0.tar.bz2  8 years and 2 months ago 2505 main cf201901
conda 35.3 kB | osx-64/r-deoptimr-1.0_6-r3.3.2_0.tar.bz2  8 years and 2 months ago 345 main cf201901
conda 33.9 kB | osx-64/r-deoptimr-1.0_6-r3.3.1_0.tar.bz2  8 years and 10 months ago 176 main cf201901
conda 33.8 kB | linux-64/r-deoptimr-1.0_6-r3.3.1_0.tar.bz2  8 years and 10 months ago 3538 main cf201901
conda 33.9 kB | osx-64/r-deoptimr-1.0_6-r3.2.2_0.tar.bz2  9 years and 1 month ago 588 main cf201901
conda 33.9 kB | linux-64/r-deoptimr-1.0_6-r3.2.2_0.tar.bz2  9 years and 1 month ago 3232 main cf201901

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