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  "Description": "Flexible general-purpose toolbox implementing genetic\nalgorithms (GAs) for stochastic optimisation. Binary,\nreal-valued, and permutation representations are available to\noptimize a fitness function, i.e. a function provided by users\ndepending on their objective function. Several genetic\noperators are available and can be combined to explore the best\nsettings for the current task. Furthermore, users can define\nnew genetic operators and easily evaluate their performances.\nLocal search using general-purpose optimisation algorithms can\nbe applied stochastically to exploit interesting regions. GAs\ncan be run sequentially or in parallel, using an explicit\nmaster-slave parallelisation or a coarse-grain islands\napproach. For more details see Scrucca (2013)\n<doi:10.18637/jss.v053.i04> and Scrucca (2017)\n<doi:10.32614/RJ-2017-008>.",
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    "gaSummary",
    "gray2binary",
    "jet.colors",
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    "persp3D",
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    "plot.gaisl",
    "print",
    "print.summary.de",
    "print.summary.ga",
    "print.summary.gaisl",
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    "repairSolution",
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    "spectral.colors",
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    "summary",
    "summary.de",
    "summary.ga",
    "summary.gaisl"
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      "title": "Genetic Algorithms",
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        "GA"
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