miesmuschel: Mixed Integer Evolution Strategies

Evolutionary black box optimization algorithms building on the 'bbotk' package. 'miesmuschel' offers both ready-to-use optimization algorithms, as well as their fundamental building blocks that can be used to manually construct specialized optimization loops. The Mixed Integer Evolution Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be implemented, as well as the multi-objective optimization algorithms NSGA-II by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.

Version: 0.0.2
Depends: paradox (≥ 0.7.1)
Imports: mlr3misc (≥ 0.5.0), checkmate (≥ 1.9.0), R6, bbotk (≥, data.table, matrixStats, lgr
Suggests: tinytest, mlr3tuning, mlr3, mlr3learners, ranger, xgboost, rpart
Published: 2022-11-16
Author: Martin Binder [aut, cre], Lennart Schneider ORCID iD [ctb], Susanne Dandl ORCID iD [ctb], Andreas Hofheinz [ctb]
Maintainer: Martin Binder <mlr.developer at mb706.com>
BugReports: https://github.com/mlr-org/miesmuschel/issues
License: MIT + file LICENSE
URL: https://github.com/mlr-org/miesmuschel
NeedsCompilation: no
Materials: README NEWS
CRAN checks: miesmuschel results


Reference manual: miesmuschel.pdf


Package source: miesmuschel_0.0.2.tar.gz
Windows binaries: r-devel: miesmuschel_0.0.2.zip, r-release: miesmuschel_0.0.1.zip, r-oldrel: miesmuschel_0.0.2.zip
macOS binaries: r-release (arm64): miesmuschel_0.0.2.tgz, r-oldrel (arm64): miesmuschel_0.0.2.tgz, r-release (x86_64): miesmuschel_0.0.2.tgz, r-oldrel (x86_64): miesmuschel_0.0.2.tgz
Old sources: miesmuschel archive


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