From 2d10017c74578b1e47e58d10a021532d44942aff Mon Sep 17 00:00:00 2001 From: Jakob Bossek Date: Mon, 14 Aug 2017 15:11:52 +0200 Subject: [PATCH] update pkgdown site --- .Rbuildignore | 1 + docs/index.html | 7 +- docs/news/index.html | 66 +++-- docs/reference/addCountingWrapper.html | 2 +- docs/reference/addLoggingWrapper.html | 276 +++++++++--------- docs/reference/autoplot.smoof_function-10.png | Bin 51175 -> 51298 bytes docs/reference/autoplot.smoof_function-2.png | Bin 37098 -> 37198 bytes docs/reference/autoplot.smoof_function-4.png | Bin 23574 -> 23537 bytes docs/reference/autoplot.smoof_function-6.png | Bin 37453 -> 37430 bytes docs/reference/autoplot.smoof_function-8.png | Bin 50905 -> 51807 bytes docs/reference/filterFunctionsByTags.html | 26 +- docs/reference/index.html | 26 +- ...BFunction-2.png => makeBBOBFunction-3.png} | Bin docs/reference/makeBBOBFunction.html | 2 +- docs/reference/makeBraninFunction-4.png | Bin 27064 -> 27104 bytes docs/reference/makeBraninFunction.html | 8 +- docs/reference/makeFunctionsByName.html | 4 +- docs/reference/makeHartmannFunction.html | 152 ++++++++++ .../makeInvertedVincentFunction.html | 139 +++++++++ .../makeModifiedRastriginFunction.html | 145 +++++++++ docs/reference/makeShekelFunction.html | 133 +++++++++ .../makeSingleObjectiveFunction-12.png | Bin 0 -> 25225 bytes .../makeSingleObjectiveFunction-4.png | Bin 0 -> 23183 bytes .../makeSingleObjectiveFunction-5.png | Bin 5739 -> 0 bytes .../makeSingleObjectiveFunction-8.png | Bin 0 -> 23585 bytes .../makeSingleObjectiveFunction.html | 7 +- docs/reference/plot3D.html | 2 +- .../visualizeParetoOptimalFront.html | 3 +- 28 files changed, 815 insertions(+), 184 deletions(-) rename docs/reference/{makeBBOBFunction-2.png => makeBBOBFunction-3.png} (100%) create mode 100644 docs/reference/makeHartmannFunction.html create mode 100644 docs/reference/makeInvertedVincentFunction.html create mode 100644 docs/reference/makeModifiedRastriginFunction.html create mode 100644 docs/reference/makeShekelFunction.html create mode 100644 docs/reference/makeSingleObjectiveFunction-12.png create mode 100644 docs/reference/makeSingleObjectiveFunction-4.png delete mode 100644 docs/reference/makeSingleObjectiveFunction-5.png create mode 100644 docs/reference/makeSingleObjectiveFunction-8.png diff --git a/.Rbuildignore b/.Rbuildignore index ff79687e..917c155f 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -21,3 +21,4 @@ man-roxygen bbob_generate_images.R appveyor.yml ^docs$ +README.html diff --git a/docs/index.html b/docs/index.html index f2d25ce8..22aa9642 100644 --- a/docs/index.html +++ b/docs/index.html @@ -39,7 +39,7 @@
  • - +
    • @@ -50,7 +50,7 @@ - +
    @@ -58,7 +58,7 @@ smoof: Single- and Multi-Objective Optimization test Functions

    This package offers an interface for objective functions in the context of (multi-objective) global optimization. It conveniently builds up on the S3 objects, i. e., an objective function is a S3 object composed of a descriptive name, the function itself, a parameter set, box constraints or other constraints, number of objectives and so on. Moreover, the package contains generators for a load of both single- and multi-objective optimization test functions which are frequently being used in the literature of (benchmarking) optimization algorithms. The bi-objective ZDT function family by Zitzler, Deb and Thiele is included as well as the popular single-objective test functions like De Jong’s function, Himmelblau function and Schwefel function. Moreover, the package offers a R interface to the C implementation of the Black-Box Optimization Benchmarking (BBOB) set of noiseless test functions.

    -

    examplary smoof functions

    +

    examplary smoof functions

    Installation instructions

    @@ -144,6 +144,7 @@

    Dev status

    • CRAN Status Badge
    • CRAN Downloads
      • +
    • CRAN Downloads
    • Build Status
    • Build status
    • Coverage Status
      • diff --git a/docs/news/index.html b/docs/news/index.html index bc65964f..d5e0bd72 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -81,22 +81,51 @@

      Change log All releases

    -
    +

    -smoof 1.5

    +smoof 1.5.1

    New features

      +
    • Added: Shekel function
      • +
    • Added: inverted Vincent function
      • +
    • Added: modified Rastrigin function
      • +
    • Added: Hartmann{3,4,6} functions
      • +
    +
    +
    +

    +Bugfixes

    +
      +
    • Better handling of additional arguments in makeFunctionsByName
      • +
    • convertTo{Minimization,Maximization} now flips sign of optima
      • +
    +
    +
    +

    +Miscelaneous

    +
      +
    • logging and couting wrapper now have class smoof_function
      • +
    +
    +
    +
    +

    +smoof 1.5

    +
    +

    +New features

    +
    • Added shortcuts {s,m}nof for generating single-/multi-objective objective functionswith numeric parameters only
    • Added: missing single-objective tag for BBOB functions
    • Added: log.scale argument for autoplot
    • Added: CITATION file
    -
    +

    -Bugfixes

    +Bugfixes
    • Fixed: precision issue caused by rPython respectively RJSONIO::toJSON, which by default strips numeric values to only 4 digits.
    • Fixed: ggplot2 warnings
      • @@ -111,9 +140,9 @@

      smoof 1.4

      -
      +

      -New features

      +New features

      • Added makeGOMOPFunction to create multi-objective test function based on a set of single objective functions.
      • Added new single-objective functions: Branin (modified version by Forrester et al. (2008))
        • @@ -123,9 +152,9 @@

      • Added possibility to draw interactive 3D surface plots via smoof::plot3D(fn, package = “plotly”)

    -
    +

    -Bugfixes

    +Bugfixes
    • Fixed: issue in formula and global optimum of BukinN2 function
    • overworked and refactored autoplot functions
      • @@ -139,9 +168,9 @@

      smoof 1.3

      -
      +

      -New features

      +New features

      • Added optional reference point ref.point for multi-objective functions
      • Reference point for ZDT functions is (11, 11)
        • @@ -157,9 +186,9 @@

        smoof 1.2

        -
        +

        -New features

        +New features

        • Added: functions convertToMaximization and convertToMinimization
        • Added: main parameter for plot and autoplot. By default the function name is used for the plot title.
          • @@ -173,9 +202,9 @@

        • Generator names now do not contain the ‘Function’ suffix

    -
    +

    -Bugfixes

    +Bugfixes
    • Fixed: autoplot and plot do not work for wrapped functions.
    • Fixed: hasConstraints for wrapped smoof functions
      • @@ -186,9 +215,9 @@

      smoof 1.1

      -
      +

      -New features

      +New features

      • Parameter set of predefined smoof function now contains a single vector parameter instead of multiple single numeric parameters. This is consistent with function calls now, since these always expect a single vector or list.
      • Added helper function get{Lower,Upper}BoxConstraints
        • @@ -202,9 +231,9 @@

      • Renamed bochachevsky function to bohachevsky.n1

    -
    +

    -Bugfixes

    +Bugfixes
    • Fixed some wrong tag assigments
    • Fixed global optimum of Giunta function
      • @@ -218,6 +247,7 @@

      Contents

        +
      • 1.5.1
      • 1.5
      • 1.4
      • 1.3
        • diff --git a/docs/reference/addCountingWrapper.html b/docs/reference/addCountingWrapper.html index 5d731d4a..a763ecca 100644 --- a/docs/reference/addCountingWrapper.html +++ b/docs/reference/addCountingWrapper.html @@ -121,7 +121,7 @@

        Examp # Here we pass a (2x5) matrix to the function with each column representing # one input vector x = matrix(runif(10), ncol = 5L) -fn(x)

      #> [1] 82.85 81.54 83.61 82.37 83.08
      print(getNumberOfEvaluations(fn))
      #> [1] 15
      +fn(x)

    #> [1] 82.84926 81.54278 83.61323 82.36655 83.08161
    print(getNumberOfEvaluations(fn))
    #> [1] 15
    #> x1 x2 -#> 1 1.000e+00 1.000e+00 -#> 2 1.100e+00 1.000e+00 -#> 3 1.000e+00 1.100e+00 -#> 4 9.000e-01 1.100e+00 -#> 5 9.500e-01 1.075e+00 -#> 6 9.000e-01 1.000e+00 -#> 7 8.500e-01 9.500e-01 -#> 8 9.500e-01 8.500e-01 -#> 9 9.375e-01 9.125e-01 -#> 10 8.000e-01 8.000e-01 -#> 11 7.000e-01 7.000e-01 -#> 12 8.000e-01 6.000e-01 -#> 13 8.125e-01 6.875e-01 -#> 14 5.500e-01 4.500e-01 -#> 15 3.500e-01 2.500e-01 -#> 16 2.500e-01 3.500e-01 -#> 17 3.875e-01 4.125e-01 -#> 18 -1.000e-01 -1.000e-01 -#> 19 -5.000e-01 -5.000e-01 -#> 20 -2.000e-01 -8.882e-16 -#> 21 -6.250e-02 6.250e-02 -#> 22 -4.125e-01 -3.875e-01 -#> 23 8.437e-02 1.656e-01 -#> 24 -2.469e-01 -2.031e-01 -#> 25 1.562e-03 7.344e-02 -#> 26 3.906e-02 2.359e-01 -#> 27 -6.523e-02 -1.602e-02 -#> 28 -1.172e-03 -5.078e-03 -#> 29 2.949e-02 -3.887e-02 -#> 30 -6.797e-02 -9.453e-02 -#> 31 -1.582e-02 3.145e-02 -#> 32 4.824e-02 4.238e-02 -#> 33 1.987e-02 2.778e-02 -#> 34 3.452e-02 -8.740e-03 -#> 35 -3.235e-03 2.140e-02 -#> 36 -2.428e-02 -1.146e-02 -#> 37 -1.324e-02 -1.651e-03 -#> 38 -1.118e-02 -2.813e-02 -#> 39 -5.221e-03 9.017e-03 -#> 40 6.849e-03 5.590e-03 -#> 41 1.826e-03 3.780e-03 -#> 42 5.875e-03 -1.032e-02 -#> 43 -2.447e-03 4.184e-03 -#> 44 5.514e-04 1.304e-02 -#> 45 -7.411e-04 -5.481e-04 -#> 46 3.532e-03 -9.523e-04 -#> 47 2.037e-03 3.318e-04 -#> 48 -5.300e-04 -3.996e-03 -#> 49 5.904e-05 -2.052e-03 -#> 50 -2.719e-03 -2.932e-03 -#> 51 8.482e-04 -4.842e-04 -#> 52 4.804e-05 1.020e-03 -#> 53 5.079e-05 2.519e-04 -#> 54 -1.538e-03 1.879e-04 -#> 55 2.515e-04 -3.162e-04 -#> 56 1.043e-03 4.838e-04 -#> 57 -2.950e-04 -2.901e-04 -#> 58 5.973e-04 2.258e-04 -#> 59 -7.190e-05 -1.611e-04 -#> 60 -2.726e-04 4.069e-04 -#> 61 1.205e-04 -1.354e-04 -#> 62 -2.220e-06 -5.484e-04 -#> 63 3.754e-05 5.179e-05
    print(log.res$obj.vals)
    #> [1] 2.000e+00 2.210e+00 2.210e+00 2.020e+00 2.058e+00 1.810e+00 1.625e+00 -#> [8] 1.625e+00 1.712e+00 1.280e+00 9.800e-01 1.000e+00 1.133e+00 5.050e-01 -#> [15] 1.850e-01 1.850e-01 3.203e-01 2.000e-02 5.000e-01 4.000e-02 7.813e-03 -#> [22] 3.203e-01 3.455e-02 1.022e-01 5.396e-03 5.719e-02 4.512e-03 2.716e-05 -#> [29] 2.380e-03 1.356e-02 1.239e-03 4.124e-03 1.167e-03 1.268e-03 4.684e-04 -#> [36] 7.209e-04 1.781e-04 9.161e-04 1.086e-04 7.816e-05 1.762e-05 1.409e-04 -#> [43] 2.349e-05 1.704e-04 8.496e-07 1.338e-05 4.261e-06 1.625e-05 4.215e-06 -#> [50] 1.599e-05 9.538e-07 1.042e-06 6.601e-08 2.402e-06 1.632e-07 1.323e-06 -#> [57] 1.712e-07 4.077e-07 3.114e-08 2.399e-07 3.285e-08 3.007e-07 4.092e-09
    log.res = getLoggedValues(fn, compact = TRUE) -print(log.res)
    #> x1 x2 y1 -#> 1 1.000e+00 1.000e+00 2.000e+00 -#> 2 1.100e+00 1.000e+00 2.210e+00 -#> 3 1.000e+00 1.100e+00 2.210e+00 -#> 4 9.000e-01 1.100e+00 2.020e+00 -#> 5 9.500e-01 1.075e+00 2.058e+00 -#> 6 9.000e-01 1.000e+00 1.810e+00 -#> 7 8.500e-01 9.500e-01 1.625e+00 -#> 8 9.500e-01 8.500e-01 1.625e+00 -#> 9 9.375e-01 9.125e-01 1.712e+00 -#> 10 8.000e-01 8.000e-01 1.280e+00 -#> 11 7.000e-01 7.000e-01 9.800e-01 -#> 12 8.000e-01 6.000e-01 1.000e+00 -#> 13 8.125e-01 6.875e-01 1.133e+00 -#> 14 5.500e-01 4.500e-01 5.050e-01 -#> 15 3.500e-01 2.500e-01 1.850e-01 -#> 16 2.500e-01 3.500e-01 1.850e-01 -#> 17 3.875e-01 4.125e-01 3.203e-01 -#> 18 -1.000e-01 -1.000e-01 2.000e-02 -#> 19 -5.000e-01 -5.000e-01 5.000e-01 -#> 20 -2.000e-01 -8.882e-16 4.000e-02 -#> 21 -6.250e-02 6.250e-02 7.813e-03 -#> 22 -4.125e-01 -3.875e-01 3.203e-01 -#> 23 8.437e-02 1.656e-01 3.455e-02 -#> 24 -2.469e-01 -2.031e-01 1.022e-01 -#> 25 1.562e-03 7.344e-02 5.396e-03 -#> 26 3.906e-02 2.359e-01 5.719e-02 -#> 27 -6.523e-02 -1.602e-02 4.512e-03 -#> 28 -1.172e-03 -5.078e-03 2.716e-05 -#> 29 2.949e-02 -3.887e-02 2.380e-03 -#> 30 -6.797e-02 -9.453e-02 1.356e-02 -#> 31 -1.582e-02 3.145e-02 1.239e-03 -#> 32 4.824e-02 4.238e-02 4.124e-03 -#> 33 1.987e-02 2.778e-02 1.167e-03 -#> 34 3.452e-02 -8.740e-03 1.268e-03 -#> 35 -3.235e-03 2.140e-02 4.684e-04 -#> 36 -2.428e-02 -1.146e-02 7.209e-04 -#> 37 -1.324e-02 -1.651e-03 1.781e-04 -#> 38 -1.118e-02 -2.813e-02 9.161e-04 -#> 39 -5.221e-03 9.017e-03 1.086e-04 -#> 40 6.849e-03 5.590e-03 7.816e-05 -#> 41 1.826e-03 3.780e-03 1.762e-05 -#> 42 5.875e-03 -1.032e-02 1.409e-04 -#> 43 -2.447e-03 4.184e-03 2.349e-05 -#> 44 5.514e-04 1.304e-02 1.704e-04 -#> 45 -7.411e-04 -5.481e-04 8.496e-07 -#> 46 3.532e-03 -9.523e-04 1.338e-05 -#> 47 2.037e-03 3.318e-04 4.261e-06 -#> 48 -5.300e-04 -3.996e-03 1.625e-05 -#> 49 5.904e-05 -2.052e-03 4.215e-06 -#> 50 -2.719e-03 -2.932e-03 1.599e-05 -#> 51 8.482e-04 -4.842e-04 9.538e-07 -#> 52 4.804e-05 1.020e-03 1.042e-06 -#> 53 5.079e-05 2.519e-04 6.601e-08 -#> 54 -1.538e-03 1.879e-04 2.402e-06 -#> 55 2.515e-04 -3.162e-04 1.632e-07 -#> 56 1.043e-03 4.838e-04 1.323e-06 -#> 57 -2.950e-04 -2.901e-04 1.712e-07 -#> 58 5.973e-04 2.258e-04 4.077e-07 -#> 59 -7.190e-05 -1.611e-04 3.114e-08 -#> 60 -2.726e-04 4.069e-04 2.399e-07 -#> 61 1.205e-04 -1.354e-04 3.285e-08 -#> 62 -2.220e-06 -5.484e-04 3.007e-07 -#> 63 3.754e-05 5.179e-05 4.092e-09
    +print(log.res$pars)
    #> x1 x2 +#> 1 1.000000e+00 1.000000e+00 +#> 2 1.100000e+00 1.000000e+00 +#> 3 1.000000e+00 1.100000e+00 +#> 4 9.000000e-01 1.100000e+00 +#> 5 9.500000e-01 1.075000e+00 +#> 6 9.000000e-01 1.000000e+00 +#> 7 8.500000e-01 9.500000e-01 +#> 8 9.500000e-01 8.500000e-01 +#> 9 9.375000e-01 9.125000e-01 +#> 10 8.000000e-01 8.000000e-01 +#> 11 7.000000e-01 7.000000e-01 +#> 12 8.000000e-01 6.000000e-01 +#> 13 8.125000e-01 6.875000e-01 +#> 14 5.500000e-01 4.500000e-01 +#> 15 3.500000e-01 2.500000e-01 +#> 16 2.500000e-01 3.500000e-01 +#> 17 3.875000e-01 4.125000e-01 +#> 18 -1.000000e-01 -1.000000e-01 +#> 19 -5.000000e-01 -5.000000e-01 +#> 20 -2.000000e-01 -8.881784e-16 +#> 21 -6.250000e-02 6.250000e-02 +#> 22 -4.125000e-01 -3.875000e-01 +#> 23 8.437500e-02 1.656250e-01 +#> 24 -2.468750e-01 -2.031250e-01 +#> 25 1.562500e-03 7.343750e-02 +#> 26 3.906250e-02 2.359375e-01 +#> 27 -6.523438e-02 -1.601563e-02 +#> 28 -1.171875e-03 -5.078125e-03 +#> 29 2.949219e-02 -3.886719e-02 +#> 30 -6.796875e-02 -9.453125e-02 +#> 31 -1.582031e-02 3.144531e-02 +#> 32 4.824219e-02 4.238281e-02 +#> 33 1.987305e-02 2.778320e-02 +#> 34 3.452148e-02 -8.740234e-03 +#> 35 -3.234863e-03 2.139893e-02 +#> 36 -2.427979e-02 -1.146240e-02 +#> 37 -1.324158e-02 -1.651001e-03 +#> 38 -1.117859e-02 -2.812805e-02 +#> 39 -5.220795e-03 9.017181e-03 +#> 40 6.848907e-03 5.590057e-03 +#> 41 1.826286e-03 3.779793e-03 +#> 42 5.875206e-03 -1.031551e-02 +#> 43 -2.446795e-03 4.184008e-03 +#> 44 5.513668e-04 1.304193e-02 +#> 45 -7.410645e-04 -5.481124e-04 +#> 46 3.532016e-03 -9.523273e-04 +#> 47 2.037314e-03 3.317565e-04 +#> 48 -5.300373e-04 -3.996149e-03 +#> 49 5.904362e-05 -2.052163e-03 +#> 50 -2.719335e-03 -2.932032e-03 +#> 51 8.481516e-04 -4.841907e-04 +#> 52 4.804339e-05 1.019860e-03 +#> 53 5.079345e-05 2.518544e-04 +#> 54 -1.538423e-03 1.879327e-04 +#> 55 2.515080e-04 -3.161599e-04 +#> 56 1.043366e-03 4.838069e-04 +#> 57 -2.949569e-04 -2.901326e-04 +#> 58 5.972584e-04 2.258271e-04 +#> 59 -7.190309e-05 -1.611427e-04 +#> 60 -2.726176e-04 4.068715e-04 +#> 61 1.204766e-04 -1.354020e-04 +#> 62 -2.219950e-06 -5.483990e-04 +#> 63 3.754010e-05 5.179101e-05
    print(log.res$obj.vals)
    #> [1] 2.000000e+00 2.210000e+00 2.210000e+00 2.020000e+00 2.058125e+00 +#> [6] 1.810000e+00 1.625000e+00 1.625000e+00 1.711562e+00 1.280000e+00 +#> [11] 9.800000e-01 1.000000e+00 1.132812e+00 5.050000e-01 1.850000e-01 +#> [16] 1.850000e-01 3.203125e-01 2.000000e-02 5.000000e-01 4.000000e-02 +#> [21] 7.812500e-03 3.203125e-01 3.455078e-02 1.022070e-01 5.395508e-03 +#> [26] 5.719238e-02 4.512024e-03 2.716064e-05 2.380447e-03 1.355591e-02 +#> [31] 1.239090e-03 4.123611e-03 1.166844e-03 1.268125e-03 4.683784e-04 +#> [36] 7.208946e-04 1.780652e-04 9.161481e-04 1.085663e-04 7.815627e-05 +#> [41] 1.762216e-05 1.409279e-04 2.349272e-05 1.703958e-04 8.496039e-07 +#> [46] 1.338207e-05 4.260709e-06 1.625014e-05 4.214860e-06 1.599159e-05 +#> [51] 9.538017e-07 1.042423e-06 6.601059e-08 2.402063e-06 1.632133e-07 +#> [56] 1.322682e-06 1.711765e-07 4.077154e-07 3.113701e-08 2.398648e-07 +#> [61] 3.284831e-08 3.007464e-07 4.091568e-09
    log.res = getLoggedValues(fn, compact = TRUE) +print(log.res)
    #> x1 x2 y1 +#> 1 1.000000e+00 1.000000e+00 2.000000e+00 +#> 2 1.100000e+00 1.000000e+00 2.210000e+00 +#> 3 1.000000e+00 1.100000e+00 2.210000e+00 +#> 4 9.000000e-01 1.100000e+00 2.020000e+00 +#> 5 9.500000e-01 1.075000e+00 2.058125e+00 +#> 6 9.000000e-01 1.000000e+00 1.810000e+00 +#> 7 8.500000e-01 9.500000e-01 1.625000e+00 +#> 8 9.500000e-01 8.500000e-01 1.625000e+00 +#> 9 9.375000e-01 9.125000e-01 1.711562e+00 +#> 10 8.000000e-01 8.000000e-01 1.280000e+00 +#> 11 7.000000e-01 7.000000e-01 9.800000e-01 +#> 12 8.000000e-01 6.000000e-01 1.000000e+00 +#> 13 8.125000e-01 6.875000e-01 1.132812e+00 +#> 14 5.500000e-01 4.500000e-01 5.050000e-01 +#> 15 3.500000e-01 2.500000e-01 1.850000e-01 +#> 16 2.500000e-01 3.500000e-01 1.850000e-01 +#> 17 3.875000e-01 4.125000e-01 3.203125e-01 +#> 18 -1.000000e-01 -1.000000e-01 2.000000e-02 +#> 19 -5.000000e-01 -5.000000e-01 5.000000e-01 +#> 20 -2.000000e-01 -8.881784e-16 4.000000e-02 +#> 21 -6.250000e-02 6.250000e-02 7.812500e-03 +#> 22 -4.125000e-01 -3.875000e-01 3.203125e-01 +#> 23 8.437500e-02 1.656250e-01 3.455078e-02 +#> 24 -2.468750e-01 -2.031250e-01 1.022070e-01 +#> 25 1.562500e-03 7.343750e-02 5.395508e-03 +#> 26 3.906250e-02 2.359375e-01 5.719238e-02 +#> 27 -6.523438e-02 -1.601563e-02 4.512024e-03 +#> 28 -1.171875e-03 -5.078125e-03 2.716064e-05 +#> 29 2.949219e-02 -3.886719e-02 2.380447e-03 +#> 30 -6.796875e-02 -9.453125e-02 1.355591e-02 +#> 31 -1.582031e-02 3.144531e-02 1.239090e-03 +#> 32 4.824219e-02 4.238281e-02 4.123611e-03 +#> 33 1.987305e-02 2.778320e-02 1.166844e-03 +#> 34 3.452148e-02 -8.740234e-03 1.268125e-03 +#> 35 -3.234863e-03 2.139893e-02 4.683784e-04 +#> 36 -2.427979e-02 -1.146240e-02 7.208946e-04 +#> 37 -1.324158e-02 -1.651001e-03 1.780652e-04 +#> 38 -1.117859e-02 -2.812805e-02 9.161481e-04 +#> 39 -5.220795e-03 9.017181e-03 1.085663e-04 +#> 40 6.848907e-03 5.590057e-03 7.815627e-05 +#> 41 1.826286e-03 3.779793e-03 1.762216e-05 +#> 42 5.875206e-03 -1.031551e-02 1.409279e-04 +#> 43 -2.446795e-03 4.184008e-03 2.349272e-05 +#> 44 5.513668e-04 1.304193e-02 1.703958e-04 +#> 45 -7.410645e-04 -5.481124e-04 8.496039e-07 +#> 46 3.532016e-03 -9.523273e-04 1.338207e-05 +#> 47 2.037314e-03 3.317565e-04 4.260709e-06 +#> 48 -5.300373e-04 -3.996149e-03 1.625014e-05 +#> 49 5.904362e-05 -2.052163e-03 4.214860e-06 +#> 50 -2.719335e-03 -2.932032e-03 1.599159e-05 +#> 51 8.481516e-04 -4.841907e-04 9.538017e-07 +#> 52 4.804339e-05 1.019860e-03 1.042423e-06 +#> 53 5.079345e-05 2.518544e-04 6.601059e-08 +#> 54 -1.538423e-03 1.879327e-04 2.402063e-06 +#> 55 2.515080e-04 -3.161599e-04 1.632133e-07 +#> 56 1.043366e-03 4.838069e-04 1.322682e-06 +#> 57 -2.949569e-04 -2.901326e-04 1.711765e-07 +#> 58 5.972584e-04 2.258271e-04 4.077154e-07 +#> 59 -7.190309e-05 -1.611427e-04 3.113701e-08 +#> 60 -2.726176e-04 4.068715e-04 2.398648e-07 +#> 61 1.204766e-04 -1.354020e-04 3.284831e-08 +#> 62 -2.219950e-06 -5.483990e-04 3.007464e-07 +#> 63 3.754010e-05 5.179101e-05 4.091568e-09
    +#> [29] "Hartmann" "Himmelblau" +#> [31] "Holder Table N. 1" "Holder Table N. 2" +#> [33] "Hosaki" "Inverted Vincent Mixture" +#> [35] "Judge" "Keane" +#> [37] "Kearfott" "McCormick" +#> [39] "Michalewicz" "Modified Rastrigin" +#> [41] "Periodic" "Double-Sum" +#> [43] "Price N. 1" "Price N. 2" +#> [45] "Price Function N. 4" "Rastrigin" +#> [47] "Rosenbrock" "Schwefel" +#> [49] "Shekel function" "Shubert" +#> [51] "Six-Hump Camel Back" "Sphere" +#> [53] "Styblinkski-Tang" "Swiler2014" +#> [55] "Three-Hump Camel" "Trecanni"
    +}
    #> Loading required package: plot3D
    print(autoplot(fn, show.optimum = TRUE))
    +#> x1 x2 +#> 1 -3.141593 12.275 +#> 2 3.141593 2.275 +#> 3 9.424778 2.475
    print(autoplot(fn, show.optimum = TRUE))
    print(autoplot(fn))
    #> Error in UseMethod("depth"): nicht anwendbare Methode für 'depth' auf Objekt der Klasse "NULL" angewendet
    +#> 1 0
    print(autoplot(fn))
    fn.num2 = makeSingleObjectiveFunction( name = "Numeric 2D", fn = function(x) sum(x^2), @@ -242,8 +242,7 @@

    Examp #> Number of parameters: 2 #> Type len Def Constr Req Tunable Trafo #> x1 numeric - - -5 to 5 - TRUE - -#> x2 numeric - - -10 to 20 - TRUE -

    print(autoplot(fn.num2)) - +#> x2 numeric - - -10 to 20 - TRUE -
    print(autoplot(fn.num2))
    fn.mixed = makeSingleObjectiveFunction( name = "Mixed 2D", fn = function(x) x$num1^2 + as.integer(as.character(x$disc1) == "a"), @@ -267,7 +266,7 @@

    Examp #> disc1 discrete - - a,b - TRUE - #> Global optimum objective value of 0.0000 at #> num1 disc1 -#> 1 0 b

    print(autoplot(fn.mixed))
    +#> 1 0 b
    print(autoplot(fn.mixed))