# A Literature Survey on Offline Automatic Algorithm Configuration
## 6 Multi-Objective META-EAs
#### 2.1.2 ParamILS
The idea behind adaptive capping is to avoid redundant runs of the target algorithm by developing bounds on the performance measure to be optimized. [PERSON] and [PERSON] [49] investigated variable neighborhood search mechanisms instead of the local search phase of ParamILS (one-exchange neighborhood), and they obtained promising results. Further to this, multi-objective extensions of BasicILS and FocusedILS are introduced in [37]. MO-ParamILS uses a multi-objective iterated local search procedure. In this extended method, a non-dominated configuration set is modified iteratively. Additionally, this property differs from the single objective ParamILS. They used MO-ParamILS to demonstrate trade-offs between solution quality and running time performance measures, and secondly between memory usage and running time. Experiments demonstrated that both are effective tools for bi-objective configuration cases, but MO-FocusedILS outperformed MO-BasicILS. For future research, they offer to apply the multi-objective configuration to multi-objective algorithms, which are difficult to configure and to develop multi-objective extensions of other automatic configurators. In the next study, [PERSON] et al. In [50], they applied the MO-ParamILS to configure a multi-objective local search algorithm applied to bi-objective permutation flow shop problems. They extended their experimental study in [51] by considering the bi-objective TSP problem and also considering large instance sets. In [52], the authors discussed the effect of correlation between optimization objectives on algorithm configuration for multi-objective algorithms, and their results proved that the performance of these algorithm configuration methods is not affected by the degree of correlation between objectives. However, the correlation between objectives and the problem set size affects the best performing configuration. One aspect of ParamILS is that it can configure only categorical parameters; otherwise, it requires discretizing numerical parameters. This can be considered a disadvantage of ParamILS, but different configuration problems, such as [53, 54], demonstrate that this is not a major obstacle to the performance of ParamILS. In the above, we provided a basic overview of applications of ParamILS, but for details of other ParamILS applications, we refer to [11, 47]. We summarized the main advantages and disadvantages of commonly used model-free algorithm configuration methods in Table 1. It must be stated that the advantages and disadvantages of the offline tuning methods that we summarized in Tables 1-3 are related to their specific underlying principles (such as the elimination method or search space) and their specific assumptions. While these advantages and disadvantages are method-specific and in different domains, we cannot present them in a correlation between the different methods. This could only be performed under very restricted pre-defined criteria, but in this case, it does not reflect the total contribution or limitations of the mentioned methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{RAVAC++} & \multirow{8}{*}{Using the entropy value of a parameter is very precious because it demonstrates how much tuning effort each parameter requires [28].} & \multirow{8}{*}{One of the weaknesses of REVAC, it is required to choose a random value from the returned interval for the parameter and it may be an inappropriate value. Additionally, so, performance differences between individual configurations cannot be demonstrated exactly.} \\  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the advantages and disadvantages of widely-used model-free algorithm configuration methods.