# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Thu May 20 09:16:00 2021
# ------------------------------------------------

package:
  name: r-ga
  version: 3.2.1
source:
  sha256: b494d970b9a2876fdb805d2ba0ac7d8a2cfd875b4e432d1c532cdb1b50eb562d
  url:
    - https://cran.r-project.org/src/contrib/Archive/GA/GA_3.2.1.tar.gz
    - https://cran.r-project.org/src/contrib/GA_3.2.1.tar.gz
build:
  merge_build_host: true
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r40ha856d6a_0
requirements:
  build:
    - m2-bash 4.3.042 5
    - m2-coreutils 8.25 102
    - m2-filesystem 2016.04 4
    - m2-gcc-libs 5.3.0 4
    - m2-gmp 6.1.0 3
    - m2-libbz2 1.0.6 3
    - m2-libiconv 1.14 3
    - m2-libintl 0.19.7 4
    - m2-make 4.1 5
    - m2-msys2-runtime 2.5.0.17080.65c939c 3
    - m2-sed 4.2.2 3
    - m2-zip 3.0 2
    - m2w64-binutils 2.25.1 5
    - m2w64-bzip2 1.0.6 6
    - m2w64-crt-git 5.0.0.4636.2595836 2
    - m2w64-gcc 5.3.0 6
    - m2w64-gcc-ada 5.3.0 6
    - m2w64-gcc-fortran 5.3.0 6
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gcc-objc 5.3.0 6
    - m2w64-gmp 6.1.0 2
    - m2w64-headers-git 5.0.0.4636.c0ad18a 2
    - m2w64-isl 0.16.1 2
    - m2w64-libiconv 1.14 6
    - m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-make 4.1.2351.a80a8b8 2
    - m2w64-mpc 1.0.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pkg-config 0.29.1 2
    - m2w64-toolchain 5.3.0 7
    - m2w64-toolchain_win-64 2.4.0 0
    - m2w64-tools-git 5.0.0.4592.90b8472 2
    - m2w64-windows-default-manifest 6.4 3
    - m2w64-winpthreads-git 5.0.0.4634.697f757 2
    - m2w64-zlib 1.2.8 10
    - msys2-conda-epoch 20160418 1
  host:
    - _r-mutex 1.0.1 anacondar_1
    - intel-openmp 2021.2.0 h57928b3_616
    - libblas 3.9.0 8_mkl
    - liblapack 3.9.0 8_mkl
    - m2w64-bwidget 1.9.10 2
    - m2w64-bzip2 1.0.6 6
    - m2w64-expat 2.1.1 2
    - m2w64-fftw 3.3.4 6
    - m2w64-flac 1.3.1 3
    - m2w64-gcc-libgfortran 5.3.0 6
    - m2w64-gcc-libs 5.3.0 7
    - m2w64-gcc-libs-core 5.3.0 7
    - m2w64-gettext 0.19.7 2
    - m2w64-gmp 6.1.0 2
    - m2w64-gsl 2.1 2
    - m2w64-libiconv 1.14 6
    - m2w64-libjpeg-turbo 1.4.2 3
    - m2w64-libogg 1.3.2 3
    - m2w64-libpng 1.6.21 2
    - m2w64-libsndfile 1.0.26 2
    - m2w64-libtiff 4.0.6 2
    - m2w64-libvorbis 1.3.5 2
    - m2w64-libwinpthread-git 5.0.0.4634.697f757 2
    - m2w64-libxml2 2.9.3 3
    - m2w64-mpfr 3.1.4 4
    - m2w64-pcre2 10.34 0
    - m2w64-speex 1.2rc2 3
    - m2w64-speexdsp 1.2rc3 3
    - m2w64-tcl 8.6.5 3
    - m2w64-tk 8.6.5 3
    - m2w64-tktable 2.10 5
    - m2w64-wineditline 2.101 5
    - m2w64-xz 5.2.2 2
    - m2w64-zlib 1.2.8 10
    - mkl 2020.4 hb70f87d_311
    - msys2-conda-epoch 20160418 1
    - r-assertthat 0.2.1 r40h6115d3f_2
    - r-base 4.0.5 hddad469_1
    - r-cli 2.5.0 r40hc72bb7e_0
    - r-codetools 0.2_18 r40hc72bb7e_0
    - r-crayon 1.4.1 r40hc72bb7e_0
    - r-fansi 0.4.2 r40h6d2157b_0
    - r-foreach 1.5.1 r40h142f84f_0
    - r-glue 1.4.2 r40h6d2157b_0
    - r-iterators 1.0.13 r40h142f84f_0
    - r-rcpp 1.0.6 r40ha856d6a_0
    - r-rcpparmadillo 0.10.4.0.0 r40h399f8d5_0
  run:
    - m2w64-gcc-libs
    - r-base >=4.0,<4.1.0a0
    - r-cli
    - r-crayon
    - r-foreach
    - r-iterators
    - r-rcpp
    - r-rcpparmadillo
test:
  commands:
    - '"%R%" -e "library(''GA'')"'
about:
  home: https://luca-scr.github.io/GA/
  license: GPL-2.0-or-later
  license_family: GPL3
  license_file:
    - D:\bld\r-ga_1621502434904\_h_env/lib/R/share/licenses/GPL-2
  summary: Flexible general-purpose toolbox implementing genetic algorithms (GAs)
    for stochastic optimisation. Binary, real-valued, and permutation representations
    are available to optimize a fitness function, i.e. a function provided by users
    depending on their objective function. Several genetic operators are available
    and can be combined to explore the best settings for the current task. Furthermore,
    users can define new genetic operators and easily evaluate their performances.
    Local search using general-purpose optimisation algorithms can be applied stochastically
    to exploit interesting regions. GAs can be run sequentially or in parallel, using
    an explicit master-slave parallelisation or a coarse-grain islands approach.
extra:
  copy_test_source_files: true
  final: true
  recipe-maintainers:
    - conda-forge/r
    - xhochy
