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kurobako benchmark example result
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report.md

Benchmark Result Report

  • Kurobako Version: 0.2.6
  • Number of Solvers: 2
  • Number of Problems: 42
  • Metrics Precedence: best value -> AUC

Please refer to "A Strategy for Ranking Optimizers using Multiple Criteria" for the ranking strategy used in this report.

Table of Contents

  1. Overall Results
  2. Individual Results
  3. Solvers
  4. Problems
  5. Studies

Overall Results

Solver Borda Firsts
Optuna 1 38
TPE 4 41

Individual Results

(1) Problem: sigopt/evalset/Xor(dim=9)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 0.993390 +- 0.005154 79.062 +- 0.223 2.099 +- 0.272
1 TPE (study) 0.989686 +- 0.007573 78.846 +- 0.655 0.154 +- 0.024

(2) Problem: sigopt/evalset/CarromTable(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 TPE (study) -22.872949 +- 1.005172 -1652.571 +- 190.729 0.052 +- 0.010
2 Optuna (study) -22.194564 +- 3.416137 -1348.154 +- 352.782 0.514 +- 0.071

(3) Problem: sigopt/evalset/DeflectedCorrugatedSpring(dim=4)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -0.717780 +- 0.182056 -22.876 +- 16.804 0.974 +- 0.108
1 TPE (study) -0.727596 +- 0.196942 -29.251 +- 19.134 0.082 +- 0.017

(4) Problem: sigopt/evalset/McCourt07(dim=6, res=12)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 TPE (study) -0.275000 +- 0.068550 -5.367 +- 6.368 0.108 +- 0.018
2 Optuna (study) -0.227778 +- 0.064310 -0.981 +- 8.374 1.447 +- 0.150

(5) Problem: sigopt/evalset/Trid(dim=6)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -39.322946 +- 6.127839 -972.224 +- 852.826 1.390 +- 0.141
1 TPE (study) -41.375532 +- 5.999828 -1136.604 +- 1061.727 0.114 +- 0.012

(6) Problem: sigopt/evalset/Hartmann3(dim=3)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -3.794874 +- 0.068767 -268.923 +- 16.981 0.714 +- 0.084
1 TPE (study) -3.771731 +- 0.143582 -266.521 +- 16.429 0.067 +- 0.015

(7) Problem: sigopt/evalset/Rastrigin(dim=8)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 51.567655 +- 9.603779 5092.247 +- 559.509 1.890 +- 0.163
1 TPE (study) 47.929794 +- 8.504059 5011.361 +- 569.197 0.131 +- 0.008

(8) Problem: sigopt/evalset/CarromTable(dim=2, int=[0])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 TPE (study) -19.382277 +- 2.139465 -1398.042 +- 240.191 0.048 +- 0.009
2 Optuna (study) -18.558809 +- 2.898384 -1124.117 +- 355.133 0.546 +- 0.097

(9) Problem: sigopt/evalset/Branin02(dim=2, int=[0])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 8.322043 +- 1.121514 906.763 +- 163.107 0.543 +- 0.086
1 TPE (study) 8.176882 +- 0.891681 898.070 +- 197.586 0.047 +- 0.008

(10) Problem: sigopt/evalset/Exponential(dim=6)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -0.988995 +- 0.009684 -74.520 +- 1.445 1.411 +- 0.160
2 TPE (study) -0.985751 +- 0.009745 -73.532 +- 1.698 0.113 +- 0.014

(11) Problem: sigopt/evalset/McCourt27(dim=3)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -1.115265 +- 0.290447 -29.629 +- 27.596 0.736 +- 0.074
1 TPE (study) -1.181162 +- 0.392760 -28.515 +- 20.999 0.065 +- 0.008

(12) Problem: sigopt/evalset/Easom(dim=4)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 17.207166 +- 4.150281 1532.213 +- 153.864 0.965 +- 0.118
1 TPE (study) 16.640832 +- 4.458301 1513.019 +- 152.896 0.081 +- 0.015

(13) Problem: sigopt/evalset/Michalewicz(dim=4)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -2.456921 +- 0.409687 -154.184 +- 24.965 1.017 +- 0.103
1 TPE (study) -2.429811 +- 0.344868 -151.232 +- 24.882 0.079 +- 0.012

(14) Problem: sigopt/evalset/Sphere(dim=7)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 2.584901 +- 1.340599 653.296 +- 206.452 1.631 +- 0.149
1 TPE (study) 2.519518 +- 1.088015 712.224 +- 149.846 0.129 +- 0.017

(15) Problem: sigopt/evalset/Tripod(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 5.096237 +- 2.344442 1012.417 +- 305.085 0.494 +- 0.058
1 TPE (study) 4.703087 +- 1.725078 955.442 +- 243.257 0.050 +- 0.008

(16) Problem: sigopt/evalset/Shekel05(dim=4)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -1.589587 +- 0.735620 -69.877 +- 19.731 0.974 +- 0.084
1 TPE (study) -2.369999 +- 1.654154 -102.969 +- 69.721 0.086 +- 0.016

(17) Problem: sigopt/evalset/Ackley(dim=3)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 5.059500 +- 1.552897 710.526 +- 117.211 0.738 +- 0.102
1 TPE (study) 5.024110 +- 1.502778 744.546 +- 142.450 0.067 +- 0.021

(18) Problem: sigopt/evalset/LennardJones6(dim=6)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -0.978761 +- 0.045789 -50.342 +- 9.442 1.409 +- 0.147
1 TPE (study) -0.964420 +- 0.065015 -51.227 +- 10.873 0.108 +- 0.014

(19) Problem: sigopt/evalset/McCourt22(dim=5)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -2.578230 +- 0.242046 -165.000 +- 23.266 1.210 +- 0.120
1 TPE (study) -2.707553 +- 0.254801 -166.977 +- 15.933 0.099 +- 0.023

(20) Problem: sigopt/evalset/Ackley(dim=11, int=[0, 1, 2])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 13.936461 +- 1.516013 1300.225 +- 72.129 2.650 +- 0.300
1 TPE (study) 14.140112 +- 1.929461 1290.064 +- 103.408 0.181 +- 0.019

(21) Problem: sigopt/evalset/Mishra06(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -1.856294 +- 0.287699 -100.549 +- 21.900 0.502 +- 0.045
1 TPE (study) -1.944638 +- 0.251441 -100.954 +- 23.131 0.049 +- 0.008

(22) Problem: sigopt/evalset/Weierstrass(dim=3)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 13.312590 +- 0.432109 1113.177 +- 31.581 0.705 +- 0.088
1 TPE (study) 13.169722 +- 0.328912 1099.970 +- 24.815 0.066 +- 0.009

(23) Problem: sigopt/evalset/Ackley(dim=5)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 8.113488 +- 1.606887 980.545 +- 158.876 1.223 +- 0.169
1 TPE (study) 8.236194 +- 1.903449 1001.801 +- 126.397 0.091 +- 0.017

(24) Problem: sigopt/evalset/Bukin06(dim=2, int=[0])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 9.722000 +- 5.270652 1883.877 +- 525.400 0.551 +- 0.063
1 TPE (study) 8.347149 +- 3.900352 1778.613 +- 446.735 0.050 +- 0.009

(25) Problem: sigopt/evalset/Schwefel20(dim=2, int=[0])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 2.805165 +- 1.868455 955.988 +- 269.244 0.614 +- 0.071
1 TPE (study) 3.147916 +- 1.906583 940.110 +- 311.947 0.049 +- 0.009

(26) Problem: sigopt/evalset/Deb02(dim=6)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -0.745466 +- 0.058212 -51.715 +- 3.635 1.418 +- 0.148
1 TPE (study) -0.731667 +- 0.071744 -51.128 +- 4.275 0.107 +- 0.014

(27) Problem: sigopt/evalset/Ackley(dim=11)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 14.277443 +- 1.794100 1315.809 +- 91.249 2.556 +- 0.265
1 TPE (study) 14.521596 +- 1.513862 1305.979 +- 98.917 0.179 +- 0.015

(28) Problem: sigopt/evalset/Sphere(dim=7, int=[0, 1, 2, 3, 4])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 2.103150 +- 1.342227 698.066 +- 210.207 1.755 +- 0.152
1 TPE (study) 2.689469 +- 1.195182 795.176 +- 214.213 0.119 +- 0.012

(29) Problem: sigopt/evalset/StyblinskiTang(dim=5)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -157.061698 +- 13.683493 -10443.805 +- 860.943 1.159 +- 0.112
1 TPE (study) -159.368114 +- 13.823497 -10983.855 +- 1194.895 0.091 +- 0.016

(30) Problem: sigopt/evalset/Ned01(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 0.076205 +- 0.070345 14.860 +- 5.824 0.501 +- 0.046
1 TPE (study) 0.038726 +- 0.088347 13.520 +- 7.614 0.048 +- 0.008

(31) Problem: sigopt/evalset/YaoLiu(dim=5)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 20.587560 +- 5.389727 2416.369 +- 411.935 1.181 +- 0.137
1 TPE (study) 22.129447 +- 6.635147 2599.144 +- 393.332 0.099 +- 0.021

(32) Problem: sigopt/evalset/Easom(dim=5)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 19.598449 +- 2.335792 1618.950 +- 77.894 1.178 +- 0.134
1 TPE (study) 19.675417 +- 2.527447 1599.711 +- 140.754 0.099 +- 0.019

(33) Problem: sigopt/evalset/Ackley(dim=3, res=1)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 4.300000 +- 1.417745 658.700 +- 91.811 0.742 +- 0.118
1 TPE (study) 3.900000 +- 1.011599 595.333 +- 128.060 0.065 +- 0.009

(34) Problem: sigopt/evalset/McCourt19(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -8.461423 +- 0.131150 -594.724 +- 32.279 0.531 +- 0.071
1 TPE (study) -8.506519 +- 0.097196 -584.351 +- 34.321 0.050 +- 0.009

(35) Problem: sigopt/evalset/Plateau(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 30.000000 +- 0.000000 2386.167 +- 8.513 0.506 +- 0.047
1 TPE (study) 30.000000 +- 0.000000 2384.400 +- 8.333 0.049 +- 0.008

(36) Problem: sigopt/evalset/McCourt01(dim=7, res=10)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 0.026667 +- 0.092856 21.297 +- 10.298 1.623 +- 0.157
1 TPE (study) 0.013333 +- 0.123108 21.433 +- 8.740 0.129 +- 0.026

(37) Problem: sigopt/evalset/Shekel07(dim=4)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -2.223391 +- 0.891697 -91.441 +- 20.872 0.978 +- 0.083
1 TPE (study) -2.352949 +- 1.194168 -95.508 +- 33.456 0.084 +- 0.015

(38) Problem: sigopt/evalset/McCourt06(dim=5, res=12)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 2.847222 +- 0.048512 234.444 +- 4.336 1.185 +- 0.126
1 TPE (study) 2.844444 +- 0.041574 234.839 +- 5.059 0.097 +- 0.014

(39) Problem: sigopt/evalset/Sargan(dim=2, int=[0])

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 0.000881 +- 0.002711 73.963 +- 37.908 0.616 +- 0.071
1 TPE (study) 0.056329 +- 0.301516 79.598 +- 53.628 0.048 +- 0.013

(40) Problem: sigopt/evalset/McCourt02(dim=7)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) -2.686687 +- 0.026472 -203.463 +- 4.287 1.677 +- 0.165
1 TPE (study) -2.691230 +- 0.027726 -204.052 +- 3.832 0.125 +- 0.018

(41) Problem: sigopt/evalset/Rastrigin(dim=8, res=0.1)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 TPE (study) 40.000000 +- 9.309493 4412.000 +- 698.185 0.141 +- 0.017
2 Optuna (study) 46.000000 +- 10.198039 4801.333 +- 713.787 1.896 +- 0.163

(42) Problem: sigopt/evalset/Schwefel20(dim=2)

Ranking Solver Best (avg +- sd) AUC (avg +- sd) Elapsed (avg +- sd)
1 Optuna (study) 3.066478 +- 1.381306 952.353 +- 255.962 0.583 +- 0.072
1 TPE (study) 2.518236 +- 1.449889 1103.380 +- 314.851 0.045 +- 0.005

Solvers

ID: 487e04184b5eeb61450126dfd6552b09ffc08402cf3183770d239fb094201cf2

recipe:

{
  "optuna": {}
}

specification:

{
  "name": "Optuna",
  "attrs": {
    "github": "https://github.com/optuna/optuna",
    "paper": "Akiba, Takuya, et al. \"Optuna: A next-generation hyperparameter optimization framework.\" Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2019.",
    "version": "optuna=2.1.0, kurobako-py=0.1.8"
  },
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONDITIONAL",
    "CONCURRENT"
  ]
}

ID: cd5a17022fd1fec0554d9192671f1901113ace2492e20d59ac0417f3666c4618

recipe:

{
  "command": {
    "path": "cargo",
    "args": [
      "run",
      "--release",
      "--example",
      "tpe-solver"
    ]
  }
}

specification:

{
  "name": "TPE",
  "attrs": {},
  "capabilities": [
    "UNIFORM_CONTINUOUS",
    "UNIFORM_DISCRETE",
    "LOG_UNIFORM_CONTINUOUS",
    "CATEGORICAL",
    "CONCURRENT"
  ]
}

Problems

ID: 762d606fd111289ffacaa9410ef618348513d70d9fe903e5d71a60978c28d213

recipe:

{
  "sigopt": {
    "name": "ACKLEY",
    "dim": 11
  }
}

specification:

{
  "name": "sigopt/evalset/Ackley(dim=11)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p6",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p7",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p8",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p9",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p10",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 1
}

ID: 5711ea99766d928a6218dbeaadd93bd28e82a9c3b19b0eac5b64e01b7f252fac

recipe:

{
  "sigopt": {
    "name": "ACKLEY",
    "dim": 11,
    "int": [
      0,
      1,
      2
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Ackley(dim=11, int=[0, 1, 2])",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "DISCRETE",
        "low": -10,
        "high": 30
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "DISCRETE",
        "low": -10,
        "high": 30
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "DISCRETE",
        "low": -10,
        "high": 30
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p6",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p7",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p8",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p9",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p10",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 48bc49268474110e0c2a181bb78990c6dbd331d61fb6e082d3bd095d4de815e2

recipe:

{
  "sigopt": {
    "name": "ACKLEY",
    "dim": 3
  }
}

specification:

{
  "name": "sigopt/evalset/Ackley(dim=3)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
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        "low": -10.0,
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      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
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        "low": -10.0,
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      },
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  ],
  "values_domain": [
    {
      "name": "Objective Value",
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}

ID: bc80da85f8186f371a2a127d82ff7136379712e311358c60678085830effc226

recipe:

{
  "sigopt": {
    "name": "ACKLEY",
    "dim": 3,
    "res": 1.0
  }
}

specification:

{
  "name": "sigopt/evalset/Ackley(dim=3, res=1)",
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    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p1",
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        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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  ],
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}

ID: 66cda488c56779bfd9d3bb1203160dce3d784c9bc293daabacf0509790e43aed

recipe:

{
  "sigopt": {
    "name": "ACKLEY",
    "dim": 5
  }
}

specification:

{
  "name": "sigopt/evalset/Ackley(dim=5)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
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        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
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        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
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        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 30.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
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      "distribution": "UNIFORM",
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  "steps": 1
}

ID: 2318f2882267c2f27d174954c9976ade13be9e978006ce076c6dac22fa7c8463

recipe:

{
  "sigopt": {
    "name": "BRANIN02",
    "dim": 2,
    "int": [
      0
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Branin02(dim=2, int=[0])",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "DISCRETE",
        "low": -5,
        "high": 15
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.0,
        "high": 15.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
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    }
  ],
  "steps": 1
}

ID: 6ab293911d0424ebb16aa03a9185f16e62c9023da1c0ccbf4353b0433fa42d10

recipe:

{
  "sigopt": {
    "name": "BUKIN06",
    "dim": 2,
    "int": [
      0
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Bukin06(dim=2, int=[0])",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "DISCRETE",
        "low": -15,
        "high": -5
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
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        "low": -3.0,
        "high": 3.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
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      "range": {
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      },
      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 0dcb70363493ed100a24a44a12d3d347aa1a0903a18e1ed8abe003df7a74355b

recipe:

{
  "sigopt": {
    "name": "CARROM_TABLE",
    "dim": 2
  }
}

specification:

{
  "name": "sigopt/evalset/CarromTable(dim=2)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
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      },
      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 20c7917b2603e9d2e6928ad861cc2b8edf702d96f5759ef27ca85c11335edf16

recipe:

{
  "sigopt": {
    "name": "CARROM_TABLE",
    "dim": 2,
    "int": [
      0
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/CarromTable(dim=2, int=[0])",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "DISCRETE",
        "low": -10,
        "high": 10
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      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
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        "type": "CONTINUOUS",
        "low": -10.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 6fd3e8ef60eec3daf630366f99491d35b6f05a4163ebb6d0b4db74926bc42ce2

recipe:

{
  "sigopt": {
    "name": "DEB02",
    "dim": 6
  }
}

specification:

{
  "name": "sigopt/evalset/Deb02(dim=6)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": 0.0,
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      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p5",
      "range": {
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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      "distribution": "UNIFORM",
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  ],
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}

ID: 1210ccbbf66a139a961e1392f8cc35351922a8f089a440ccd54af4577e5f368b

recipe:

{
  "sigopt": {
    "name": "DEFLECTED_CORRUGATED_SPRING",
    "dim": 4
  }
}

specification:

{
  "name": "sigopt/evalset/DeflectedCorrugatedSpring(dim=4)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
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        "low": 0.0,
        "high": 7.5
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
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        "low": 0.0,
        "high": 7.5
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
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        "low": 0.0,
        "high": 7.5
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
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        "low": 0.0,
        "high": 7.5
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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  ],
  "steps": 1
}

ID: 30f46f4f59c9a6b9ba33e0dadd611080e606ff70a844b2c68bfeb66a7a07e405

recipe:

{
  "sigopt": {
    "name": "EASOM",
    "dim": 4
  }
}

specification:

{
  "name": "sigopt/evalset/Easom(dim=4)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "CONTINUOUS",
        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: a39b57cec0151c892256bc8386f43dc15e83c2a985c9761fbf46f0abc541307e

recipe:

{
  "sigopt": {
    "name": "EASOM",
    "dim": 5
  }
}

specification:

{
  "name": "sigopt/evalset/Easom(dim=5)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": -100.0,
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      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
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        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
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        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
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        "low": -100.0,
        "high": 20.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 29849239fd4b7b5d4543cec4bc2cbb857fa41fa8b4147a765850b43409317dc1

recipe:

{
  "sigopt": {
    "name": "EXPONENTIAL",
    "dim": 6
  }
}

specification:

{
  "name": "sigopt/evalset/Exponential(dim=6)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": -0.7,
        "high": 0.2
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      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -0.7,
        "high": 0.2
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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        "low": -0.7,
        "high": 0.2
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
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        "low": -0.7,
        "high": 0.2
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      "distribution": "UNIFORM",
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    },
    {
      "name": "p4",
      "range": {
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        "low": -0.7,
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      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
      "range": {
        "type": "CONTINUOUS",
        "low": -0.7,
        "high": 0.2
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 1e08466d41a46890e37413c5f989e30c6f2155b3e3e99721997e6d9ed29148d5

recipe:

{
  "sigopt": {
    "name": "HARTMANN3",
    "dim": 3
  }
}

specification:

{
  "name": "sigopt/evalset/Hartmann3(dim=3)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": 0.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 1
}

ID: 4cc33f4e2ff5625738795af29a4d94fa607d5f5949ece8ad44b73b1e63b63339

recipe:

{
  "sigopt": {
    "name": "LENNARD_JONES6",
    "dim": 6
  }
}

specification:

{
  "name": "sigopt/evalset/LennardJones6(dim=6)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "CONTINUOUS",
        "low": -3.0,
        "high": 3.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -3.0,
        "high": 3.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
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        "low": -3.0,
        "high": 3.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p3",
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        "low": -3.0,
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      "distribution": "UNIFORM",
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    },
    {
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        "low": -3.0,
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    {
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  ],
  "values_domain": [
    {
      "name": "Objective Value",
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      "distribution": "UNIFORM",
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}

ID: ccf73084ef5dbe3746e1d5252d6bfaa643bc1ece3358a4c77decf923d8e0facb

recipe:

{
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    "name": "MC_COURT01",
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    "res": 10.0
  }
}

specification:

{
  "name": "sigopt/evalset/McCourt01(dim=7, res=10)",
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    },
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    },
    {
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    },
    {
      "name": "p6",
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  ],
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ID: e7895b7bb5dac94288be96e61ad121d23e185003eaefd87ba5e1023a6132104e

recipe:

{
  "sigopt": {
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}

specification:

{
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    },
    {
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  ],
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ID: cff13c7e9a01ac1b8cce1a957f7caa7cc6d438ced9a8c25b658215794acc6d92

recipe:

{
  "sigopt": {
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}

specification:

{
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ID: 1464bdea4ea2f345666eae6ecdda641b6a196281174f5ba8007a8e8a2bec532e

recipe:

{
  "sigopt": {
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}

specification:

{
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ID: c402d6ef019d823cfda5127b120f2bd5eb3e021edc112118ae0c9f07e72de789

recipe:

{
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}

specification:

{
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ID: 541f33a97b45aa4a10f3197b56751eb385ded69f15d40453f8b7dee5ba7882a8

recipe:

{
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}

specification:

{
  "name": "sigopt/evalset/McCourt22(dim=5)",
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ID: 2fef32d714633c85c412e13e086a1ec8df60117de097a4209b590376b1787729

recipe:

{
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}

specification:

{
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ID: 3902a3f083b3cb4c95db2c0166a0a337063e4555d7a2f9d2d31c367d32758f4e

recipe:

{
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}

specification:

{
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ID: 59c429b4a300b87b138a006f1128ff1d16e2c50a9c909a88ef514a10e3c80148

recipe:

{
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}

specification:

{
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ID: 862877d4e1f14c9bea4f924a5c6366938cc8e2aadd70df465af0586d67a99f8b

recipe:

{
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}

specification:

{
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ID: c6e62d2a584679ad6fcdec6abc3bab8ab394e333a01e662053a28c2837ef57a9

recipe:

{
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    "name": "PLATEAU",
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}

specification:

{
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  "params_domain": [
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ID: 1eb7796f1b2d5f244bfd8eefd6b3d97091d460e9ab56c61d71a29ad15e7fa0da

recipe:

{
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    "name": "RASTRIGIN",
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}

specification:

{
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    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
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      "name": "p6",
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    {
      "name": "p7",
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      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 1
}

ID: ec359d6b8722b456204bb2f41274079d5c5c0e08a6b3db98a762d3501a380e96

recipe:

{
  "sigopt": {
    "name": "RASTRIGIN",
    "dim": 8,
    "res": 0.1
  }
}

specification:

{
  "name": "sigopt/evalset/Rastrigin(dim=8, res=0.1)",
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    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
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        "low": -5.0,
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    },
    {
      "name": "p1",
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        "low": -5.0,
        "high": 2.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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        "low": -2.0,
        "high": 5.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p3",
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        "low": -2.0,
        "high": 5.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
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        "low": -5.0,
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p5",
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        "low": -5.0,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p6",
      "range": {
        "type": "CONTINUOUS",
        "low": -2.0,
        "high": 5.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p7",
      "range": {
        "type": "CONTINUOUS",
        "low": -2.0,
        "high": 5.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
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      "distribution": "UNIFORM",
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}

ID: e014fb18e27703930d93dd7e7ee8178ce5ffad97027d013fac90c263049c70d7

recipe:

{
  "sigopt": {
    "name": "SARGAN",
    "dim": 2,
    "int": [
      0
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Sargan(dim=2, int=[0])",
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    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": -2,
        "high": 4
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p1",
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        "low": -2.0,
        "high": 4.0
      },
      "distribution": "UNIFORM",
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    }
  ],
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      "distribution": "UNIFORM",
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    }
  ],
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}

ID: fcbd60ae002525d084f788a792305d491f32a61ac99ac711a9cfe9d1860f8d34

recipe:

{
  "sigopt": {
    "name": "SCHWEFEL20",
    "dim": 2
  }
}

specification:

{
  "name": "sigopt/evalset/Schwefel20(dim=2)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
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        "low": -60.0,
        "high": 100.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": -60.0,
        "high": 100.0
      },
      "distribution": "UNIFORM",
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    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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      "distribution": "UNIFORM",
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  ],
  "steps": 1
}

ID: 6de30105fa47b8ecbda30c3676ee059bcfcc221b82a83736bb8b479f4559da6a

recipe:

{
  "sigopt": {
    "name": "SCHWEFEL20",
    "dim": 2,
    "int": [
      0
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Schwefel20(dim=2, int=[0])",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "DISCRETE",
        "low": -60,
        "high": 100
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
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        "low": -60.0,
        "high": 100.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
      "range": {
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      "distribution": "UNIFORM",
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    }
  ],
  "steps": 1
}

ID: 47fe6e07816771591c8ea2640dea44bce93ff9171060ac4c3ece8b1c71a32aa2

recipe:

{
  "sigopt": {
    "name": "SHEKEL05",
    "dim": 4
  }
}

specification:

{
  "name": "sigopt/evalset/Shekel05(dim=4)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": -0.0,
        "high": 10.0
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      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": -0.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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        "low": -0.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
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        "low": -0.0,
        "high": 10.0
      },
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    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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  ],
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}

ID: cd5ee5f8e7c0328b28837d220987f3ed318887b201e2dccd24559937f37c215a

recipe:

{
  "sigopt": {
    "name": "SHEKEL07",
    "dim": 4
  }
}

specification:

{
  "name": "sigopt/evalset/Shekel07(dim=4)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
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    },
    {
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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        "low": -0.0,
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p3",
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        "low": -0.0,
        "high": 10.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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  ],
  "steps": 1
}

ID: 429f7ada6a8f5648aee7930fdf1f3af0807ba052ca921cac32dc102c3d6d32f2

recipe:

{
  "sigopt": {
    "name": "SPHERE",
    "dim": 7
  }
}

specification:

{
  "name": "sigopt/evalset/Sphere(dim=7)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
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      "range": {
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        "low": -5.12,
        "high": 2.12
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      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
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        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
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        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
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        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p6",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
      "constraint": null
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  ],
  "values_domain": [
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      "name": "Objective Value",
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      "distribution": "UNIFORM",
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  ],
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}

ID: 7a9b62682ddee5ac08c93669255954f154b4bae095dc877c2b478b7d20f9b45d

recipe:

{
  "sigopt": {
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    "dim": 7,
    "int": [
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      1,
      2,
      3,
      4
    ]
  }
}

specification:

{
  "name": "sigopt/evalset/Sphere(dim=7, int=[0, 1, 2, 3, 4])",
  "attrs": {
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    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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      "name": "p0",
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        "low": -5,
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      "distribution": "UNIFORM",
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    },
    {
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        "low": -5,
        "high": 2
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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      "distribution": "UNIFORM",
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    },
    {
      "name": "p3",
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p4",
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      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
      "range": {
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        "low": -5.12,
        "high": 2.12
      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p6",
      "range": {
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        "low": -5.12,
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      },
      "distribution": "UNIFORM",
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  ],
  "values_domain": [
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      "name": "Objective Value",
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  ],
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ID: 847114b901d551887b3bb6e0686324852e2cc8c4fe1ac75e8ff018da13b1023e

recipe:

{
  "sigopt": {
    "name": "STYBLINSKI_TANG",
    "dim": 5
  }
}

specification:

{
  "name": "sigopt/evalset/StyblinskiTang(dim=5)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
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  "params_domain": [
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    {
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      "distribution": "UNIFORM",
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    {
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  "values_domain": [
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      "name": "Objective Value",
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  "steps": 1
}

ID: 15ca748208253e4a2e35adf06a8f7feb49188210b02e1cd08702d54c9267a4f6

recipe:

{
  "sigopt": {
    "name": "TRID",
    "dim": 6
  }
}

specification:

{
  "name": "sigopt/evalset/Trid(dim=6)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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    {
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      },
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    {
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      "distribution": "UNIFORM",
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      },
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      "constraint": null
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    {
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      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
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        "low": 0.0,
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      },
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    }
  ],
  "values_domain": [
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  ],
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}

ID: 458dd11a95505ddb3a8d96d492dbc35b0de444d84fe0bf3fef739d02baeaf425

recipe:

{
  "sigopt": {
    "name": "TRIPOD",
    "dim": 2
  }
}

specification:

{
  "name": "sigopt/evalset/Tripod(dim=2)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
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  "params_domain": [
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        "low": -100.0,
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    {
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  ],
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ID: 6396ba82fa5de7995ce66e0eed9448e5882508432c8b324d52c5f11a79f16a78

recipe:

{
  "sigopt": {
    "name": "WEIERSTRASS",
    "dim": 3
  }
}

specification:

{
  "name": "sigopt/evalset/Weierstrass(dim=3)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
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    {
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      },
      "distribution": "UNIFORM",
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    },
    {
      "name": "p2",
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        "low": -0.5,
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      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
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      "name": "Objective Value",
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      "distribution": "UNIFORM",
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  ],
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}

ID: 099c4ef27c777d65a669d88cd599c9e679f956bd04859ed85d2c9403ec82200c

recipe:

{
  "sigopt": {
    "name": "XOR",
    "dim": 9
  }
}

specification:

{
  "name": "sigopt/evalset/Xor(dim=9)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
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  "params_domain": [
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      "name": "p0",
      "range": {
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        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p5",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p6",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p7",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p8",
      "range": {
        "type": "CONTINUOUS",
        "low": -1.0,
        "high": 1.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 1
}

ID: a0d7aa2cea146419075791ef31a3e4b50dcefbca3e9b2c52d766f0b000607e67

recipe:

{
  "sigopt": {
    "name": "YAO_LIU",
    "dim": 5
  }
}

specification:

{
  "name": "sigopt/evalset/YaoLiu(dim=5)",
  "attrs": {
    "github": "https://github.com/sigopt/evalset",
    "paper": "Dewancker, Ian, et al. \"A strategy for ranking optimization methods using multiple criteria.\" Workshop on Automatic Machine Learning. 2016.",
    "version": "kurobako_problems=0.1.10"
  },
  "params_domain": [
    {
      "name": "p0",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p1",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p2",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p3",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    },
    {
      "name": "p4",
      "range": {
        "type": "CONTINUOUS",
        "low": -5.12,
        "high": 2.0
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "values_domain": [
    {
      "name": "Objective Value",
      "range": {
        "type": "CONTINUOUS"
      },
      "distribution": "UNIFORM",
      "constraint": null
    }
  ],
  "steps": 1
}

Studies

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