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kurobako benchmark example result

# 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.

## 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
}```

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"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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",
"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
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### ID: bc80da85f8186f371a2a127d82ff7136379712e311358c60678085830effc226

recipe:

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

specification:

```{
"name": "sigopt/evalset/Ackley(dim=3, res=1)",
"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
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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",
"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
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

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",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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",
"range": {
"type": "CONTINUOUS",
"low": -3.0,
"high": 3.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

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
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -10.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"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: 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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 7.5
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 7.5
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 7.5
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 7.5
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

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",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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": [
{
"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",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -100.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"low": -0.7,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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
}```

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": {
"type": "CONTINUOUS",
"low": -3.0,
"high": 3.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -3.0,
"high": 3.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -3.0,
"high": 3.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"low": -3.0,
"high": 3.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### ID: ccf73084ef5dbe3746e1d5252d6bfaa643bc1ece3358a4c77decf923d8e0facb

recipe:

```{
"sigopt": {
"name": "MC_COURT01",
"dim": 7,
"res": 10.0
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt01(dim=7, res=10)",
"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": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p6",
"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
}```

recipe:

```{
"sigopt": {
"name": "MC_COURT02",
"dim": 7
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt02(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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p6",
"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: cff13c7e9a01ac1b8cce1a957f7caa7cc6d438ced9a8c25b658215794acc6d92

recipe:

```{
"sigopt": {
"name": "MC_COURT06",
"dim": 5,
"res": 12.0
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt06(dim=5, res=12)",
"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": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"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: 1464bdea4ea2f345666eae6ecdda641b6a196281174f5ba8007a8e8a2bec532e

recipe:

```{
"sigopt": {
"name": "MC_COURT07",
"dim": 6,
"res": 12.0
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt07(dim=6, res=12)",
"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": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"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: c402d6ef019d823cfda5127b120f2bd5eb3e021edc112118ae0c9f07e72de789

recipe:

```{
"sigopt": {
"name": "MC_COURT19",
"dim": 2
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt19(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": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"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: 541f33a97b45aa4a10f3197b56751eb385ded69f15d40453f8b7dee5ba7882a8

recipe:

```{
"sigopt": {
"name": "MC_COURT22",
"dim": 5
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt22(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": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"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: 2fef32d714633c85c412e13e086a1ec8df60117de097a4209b590376b1787729

recipe:

```{
"sigopt": {
"name": "MC_COURT27",
"dim": 3
}
}```

specification:

```{
"name": "sigopt/evalset/McCourt27(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",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 1.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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: 3902a3f083b3cb4c95db2c0166a0a337063e4555d7a2f9d2d31c367d32758f4e

recipe:

```{
"sigopt": {
"name": "MICHALEWICZ",
"dim": 4
}
}```

specification:

```{
"name": "sigopt/evalset/Michalewicz(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": 0.0,
"high": 3.141592653589793
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 3.141592653589793
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 3.141592653589793
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 3.141592653589793
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### ID: 59c429b4a300b87b138a006f1128ff1d16e2c50a9c909a88ef514a10e3c80148

recipe:

```{
"sigopt": {
"name": "MISHRA06",
"dim": 2
}
}```

specification:

```{
"name": "sigopt/evalset/Mishra06(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": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

recipe:

```{
"sigopt": {
"name": "NED01",
"dim": 2
}
}```

specification:

```{
"name": "sigopt/evalset/Ned01(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": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

recipe:

```{
"sigopt": {
"name": "PLATEAU",
"dim": 2
}
}```

specification:

```{
"name": "sigopt/evalset/Plateau(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": -2.34,
"high": 5.12
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -2.34,
"high": 5.12
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

recipe:

```{
"sigopt": {
"name": "RASTRIGIN",
"dim": 8
}
}```

specification:

```{
"name": "sigopt/evalset/Rastrigin(dim=8)",
"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": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -2.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -2.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"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",
"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)",
"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": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -2.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -2.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 2.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"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",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

recipe:

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

specification:

```{
"name": "sigopt/evalset/Sargan(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": -2,
"high": 4
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -2.0,
"high": 4.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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": {
"type": "CONTINUOUS",
"low": -60.0,
"high": 100.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -60.0,
"high": 100.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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",
"range": {
"type": "CONTINUOUS",
"low": -60.0,
"high": 100.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -0.0,
"high": 10.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -5.12,
"high": 2.12
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"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",
"range": {
"type": "CONTINUOUS",
"low": -5.12,
"high": 2.12
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"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
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### ID: 7a9b62682ddee5ac08c93669255954f154b4bae095dc877c2b478b7d20f9b45d

recipe:

```{
"sigopt": {
"name": "SPHERE",
"dim": 7,
"int": [
0,
1,
2,
3,
4
]
}
}```

specification:

```{
"name": "sigopt/evalset/Sphere(dim=7, int=[0, 1, 2, 3, 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": "DISCRETE",
"low": -5,
"high": 2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "DISCRETE",
"low": -5,
"high": 2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "DISCRETE",
"low": -5,
"high": 2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "DISCRETE",
"low": -5,
"high": 2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "DISCRETE",
"low": -5,
"high": 2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"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
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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"
},
"params_domain": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": -5.0,
"high": 5.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p3",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p4",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p5",
"range": {
"type": "CONTINUOUS",
"low": 0.0,
"high": 20.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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"
},
"params_domain": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -100.0,
"high": 100.0
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -100.0,
"high": 100.0
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"low": -0.5,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p1",
"range": {
"type": "CONTINUOUS",
"low": -0.5,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
},
{
"name": "p2",
"range": {
"type": "CONTINUOUS",
"low": -0.5,
"high": 0.2
},
"distribution": "UNIFORM",
"constraint": null
}
],
"values_domain": [
{
"name": "Objective Value",
"range": {
"type": "CONTINUOUS"
},
"distribution": "UNIFORM",
"constraint": null
}
],
"steps": 1
}```

### 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"
},
"params_domain": [
{
"name": "p0",
"range": {
"type": "CONTINUOUS",
"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
}```