Package: DEoptimR 1.2-0

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).

Authors:Eduardo L. T. Conceicao [aut, cre], Martin Maechler [ctb]

DEoptimR_1.2-0.tar.gz
DEoptimR_1.2-0.zip(r-4.7)DEoptimR_1.2-0.zip(r-4.6)DEoptimR_1.2-0.zip(r-4.5)
DEoptimR_1.2-0.tgz(r-4.6-any)DEoptimR_1.2-0.tgz(r-4.5-any)
DEoptimR_1.2-0.tar.gz(r-4.7-any)DEoptimR_1.2-0.tar.gz(r-4.6-any)
DEoptimR_1.2-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
DEoptimR/json (API)

# Install 'DEoptimR' in R:
install.packages('DEoptimR', repos = c('https://eduardotrincaoconceicao.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

7.62 score 1 stars 503 packages 77 scripts 89k downloads 2 mentions 3 exports 0 dependencies

Last updated from:9054d610a6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK156
linux-release-x86_64OK106
macos-release-arm64OK162
macos-oldrel-arm64OK126
windows-develOK92
windows-releaseOK70
windows-oldrelOK66
wasm-releaseOK91

Exports:JDEoptimNCDEoptimSPJDEoptim

Dependencies: