### Table of Contents

# Applications

## Nuclear criticality safety

*NKO participants: C. Chevalier, Y. Richet (IRSN)*

Unconstrained single objective optimization

2 variables, 75*75 data points, 40 noise levels

## Video game data

*NKO participant: M. Preuss*

#### Test case A

22D highly noisy data from the car setup optimization competition setting
(see manual concerning used programs and list of parameters), performed with the standard car and bot on track wheel-2
(Suzuka F1).

The objective function is the -1 * distance travelled in 2000 tics (40 s) divided by 2000 if the damage is at most 100, and the damage else (values up to 10000) according to recommendations in the diploma thesis of Markus Kemmerling (in German), see also this paper: Automatic Adaptation to Generated Content Via Car Setup Optimization in TORCS

torcsdata.zip contains the following files:

inputs125p.txt | Sample locations 125 points for 4×125, LHS-distributed) |

inputs250p.txt | Sample locations 250 points for 2×250, LHS-distributed) |

inputs500p.txt | Sample locations 500 points for 1×500, LHS-distributed) |

results-125p-1-4.txt | Results for inputs125p.txt, in a row (1-125,1-125,1-125,1-125) |

results-250p-1-2.txt | Results for inputs125p.txt, in a row (1-250,1-250) |

results-500p.txt | Results for inputs500p.txt |

newTorcsValidation2.txt | Validation data (440 valid points, LHS-distributed) |

Note that if the evaluation of a car was not finished due to damage (exceeding 10000), the result value is NA. In the validation set, we removed all cars where this happened.

## Hydrogeologic Testcase: well placement

*NKO participant: C. Bürger*

To be found: Optimal drinking water well locations (xi and yi coordinates) and extraction rate(Qi) for a number of wells, ntot, with i = 1,…ntot.

The optimization problem dimension is variable: 3*ntot (ntot between one and five). The noise comes from uncertainties on the underground hydraulic conductivity. The problem has boundary constraints and three non-trivial constraints, which can be taken into account with a penalization technique.

Technical details:

- the application runs in Matlab and calls a couple of batch scripts and Windows executables

- a detailed notice is provided

- an example of matlab function for optimization with cma-es is provided

- all the files are downloadable here:

realizations1.zip realizations2.zip testcasebasedonerlangensite.zip

## Plant irigation problem

*NKO participant: M. de Paly*

1000 realizations, 15 to 100 variables (irrigation times and amounts)

## BBOB test case

(C++ / Java with matlab interface)

## SPO test case

*NKO participant: M. Preuss*

## Financial test case

*NKO participant: Didier Rullière*