{
  "channels": [
    "conda-forge",
    "defaults"
  ],
  "conda_build_version": "3.18.9",
  "conda_private": false,
  "conda_version": "4.7.10",
  "env_vars": {
    "CIO_TEST": "<not set>"
  },
  "extra": {
    "copy_test_source_files": true,
    "final": true,
    "recipe-maintainers": [
      "conda-forge/r",
      "xhochy"
    ]
  },
  "home": "https://luca-scr.github.io/GA/",
  "identifiers": [],
  "keywords": [],
  "license": "GPL (>= 2)",
  "license_family": "GPL3",
  "license_file": "D:\\bld\\r-ga_1564242948435\\_h_env/lib/R/share/licenses/GPL-2",
  "root_pkgs": [
    "anaconda-client 1.7.2 py_0",
    "asn1crypto 0.24.0 py36_1003",
    "attrs 19.1.0 py_0",
    "beautifulsoup4 4.8.0 py36_0",
    "bzip2 1.0.8 hfa6e2cd_0",
    "ca-certificates 2019.6.16 hecc5488_0",
    "certifi 2019.6.16 py36_1",
    "cffi 1.12.3 py36hb32ad35_0",
    "chardet 3.0.4 py36_1003",
    "click 7.0 py_0",
    "clyent 1.2.2 py_1",
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    "conda-build 3.18.9 py36_0",
    "conda-env 2.6.0 1",
    "conda-forge-ci-setup 2.3.2 py36_0",
    "conda-package-handling 1.3.11 py36_0",
    "console_shortcut 0.1.1 3",
    "cryptography 2.7 py36hb32ad35_0",
    "decorator 4.4.0 py_0",
    "filelock 3.0.10 py_0",
    "glob2 0.7 py_0",
    "idna 2.8 py36_1000",
    "ipython_genutils 0.2.0 py_1",
    "jinja2 2.10.1 py_0",
    "jsonschema 3.0.1 py36_0",
    "jupyter_core 4.4.0 py_0",
    "libarchive 3.3.3 h4890af2_1006",
    "libiconv 1.15 hfa6e2cd_1005",
    "liblief 0.9.0 ha925a31_2",
    "libxml2 2.9.9 h9ce36c8_2",
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    "powershell_shortcut 0.0.1 2",
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    "pycosat 0.6.3 py36hfa6e2cd_1001",
    "pycparser 2.19 py36_1",
    "pyopenssl 19.0.0 py36_0",
    "pyrsistent 0.15.3 py36hfa6e2cd_0",
    "pysocks 1.7.0 py36_0",
    "python 3.6.7 he025d50_1005",
    "python-dateutil 2.8.0 py_0",
    "python-libarchive-c 2.8 py36_1004",
    "pytz 2019.1 py_0",
    "pywin32 224 py36hfa6e2cd_1000",
    "pyyaml 5.1.1 py36hfa6e2cd_0",
    "requests 2.22.0 py36_1",
    "ruamel_yaml 0.15.71 py36hfa6e2cd_1000",
    "setuptools 41.0.1 py36_0",
    "six 1.12.0 py36_1000",
    "soupsieve 1.9.2 py36_0",
    "sqlite 3.27.2 he774522_0",
    "tqdm 4.32.2 py_0",
    "traitlets 4.3.2 py36_1000",
    "urllib3 1.25.3 py36_0",
    "vc 14.1 h0510ff6_4",
    "vs2008_express_vc_python_patch 1.0.0 0",
    "vs2015_runtime 14.15.26706 h3a45250_0",
    "wheel 0.33.4 py36_0",
    "wincertstore 0.2 py36_1002",
    "win_inet_pton 1.1.0 py36_0",
    "xz 5.2.4 h2fa13f4_1001",
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    "zlib 1.2.11 h2fa13f4_1005",
    "zstd 1.4.0 hd8a0e53_0"
  ],
  "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.",
  "tags": []
}