DEoptimR: Differential Evolution Optimization in Pure R

Differential Evolution (DE) stochastic heuristic algorithms for global optimization of problems with and without general constraints. The aim is to curate a collection of its 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 provides implementations of the algorithms 'jDE' by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873> for multimodal optimization (single-objective problems with multiple solutions).

Version:	1.1-3
Imports:	stats
Enhances:	robustbase
Published:	2023-10-07
DOI:	10.32614/CRAN.package.DEoptimR
Author:	Eduardo L. T. Conceicao [aut, cre], Martin Maechler [ctb]
Maintainer:	Eduardo L. T. Conceicao <mail at eduardoconceicao.org>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Materials:	NEWS
In views:	Optimization
CRAN checks:	DEoptimR results

Documentation:

Reference manual:

DEoptimR.pdf

Downloads:

Package source:	DEoptimR_1.1-3.tar.gz
Windows binaries:	r-devel: DEoptimR_1.1-3.zip, r-release: DEoptimR_1.1-3.zip, r-oldrel: DEoptimR_1.1-3.zip
macOS binaries:	r-release (arm64): DEoptimR_1.1-3.tgz, r-oldrel (arm64): DEoptimR_1.1-3.tgz, r-release (x86_64): DEoptimR_1.1-3.tgz, r-oldrel (x86_64): DEoptimR_1.1-3.tgz
Old sources:	DEoptimR archive

Reverse dependencies:

Reverse depends:	RobustAFT
Reverse imports:	dtangle, OssaNMA, robustbase, ROI.plugin.deoptim
Reverse suggests:	ADMUR, cxr, MSCMT

Linking:

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