# This file created by conda-build 3.18.9
# meta.yaml template originally from:
# D:\a\1\s\recipe, last modified Sat Jul 27 15:52:35 2019
# ------------------------------------------------

package:
    name: r-ga
    version: '3.2'
source:
    sha256: c7a001b9354a3ea6280362ddb2d1e5dc7ede2bc324fd9564a58c42ccfe6a75ec
    url:
        - https://cran.r-project.org/src/contrib/Archive/GA/GA_3.2.tar.gz
        - https://cran.r-project.org/src/contrib/GA_3.2.tar.gz
build:
    merge_build_host: true
    number: '1'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h796a38f_1
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 2019.4 245
        - libblas 3.8.0 8_mkl
        - liblapack 3.8.0 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 4
        - m2w64-mpfr 3.1.4 4
        - m2w64-pcre 8.38 2
        - 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 2019.4 245
        - msys2-conda-epoch 20160418 1
        - r-assertthat 0.2.1 r36h6115d3f_1
        - r-base 3.6.1 he73377c_3
        - r-cli 1.1.0 r36h6115d3f_1
        - r-codetools 0.2_16 r36h6115d3f_1001
        - r-crayon 1.3.4 r36h6115d3f_1002
        - r-foreach 1.4.4 r36h6115d3f_1002
        - r-iterators 1.0.12 r36h6115d3f_0
        - r-rcpp 1.0.2 r36h796a38f_0
        - r-rcpparmadillo 0.9.500.2.0 r36h796a38f_1
    run:
        - m2w64-gcc-libs
        - r-base >=3.6,<3.7.0a0
        - 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)
    license_family: GPL3
    license_file: D:\bld\r-ga_1564242948435\_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
