# A Literature Survey on Offline Automatic Algorithm Configuration
## 4 Other Studies
### Other Proposed Algorithm Configuration Methods  
In the configuration problems, if all parameters of the target algorithm are only continuous or integer parameters, a continuous black-box optimizer, such as CMA-ES [111], BOBYQA [112], or MADS [113], can be used as a parameter tuning approach when combined with an evaluation method (racing or repeated evaluation).  
The MADS (mesh-adaptive direct search) method is suggested by [PERSON] and [PERSON] [113] for the configuration of continuous optimization algorithms, and it is guaranteed to converge to a local optimum of the cost function. [PERSON] et al. [114] extend this method to make it more configurable and introduce the primary Opal framework. They continued to extend this Opal framework in their further studies [115; 116].  
[PERSON] et al. [117] evaluated continuous optimization algorithms including CMA-ES, BOBYQA, and MADS algorithms enhanced with uniform random sampling (URS) and the sampling method used in iterated F-Race (IRS). They found that for a few numbers of parameters, BOBYQA works best; however, when the number of parameters is large, CMA-ES is the best working one. After this study, [PERSON] et al. [118] proposed using a post-selection method. In the first step, a small number of evaluations per configuration are evaluated by the numerical optimizers. In the second step, the most promising parameter values are tested by a racing process. More than one elite configuration can be obtained by this method. The drawbacks and advantages of this method are discussed in [118] and [13].  
[PERSON] and [PERSON] [119] proposed a novel algorithm configuration method, the golden parameter search algorithm (GPS), which integrates add-ons for algorithm configurators (such as racing or intensification mechanisms) with an improved golden section search [120]. The GPS method optimizes each parameter semi-independently in parallel and has two assumptions: that numeric parameters are uni-modal and that there are no strong interactions between most of the parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compared Methods** & **Tuned Algorithm** & **Target Problem** & **Performance Criteria** \\ \hline ParamILS, SMAC, & Mixed-integer solver CPLEX & MIP benchmark instances & Optimality gap \\ D-SMAC [8] & & & \\ \hline TB- SPO, FocusedIIS, & SAPS (dynamic local search algorithm for (SAT) problem) & quasigroup completion problem (QCP) and the graph-coloring problem & minimize the median number of SAPS search steps required to solve the instance and runtime \\ \hline F-Race, Revac, ParamILS and SPO [12] & A standard genetic algorithm (SGA) & Benchmark test functions & Amount of evaluations to find the optimum \\ \hline MetaTuner, Irace, ParamILS & Differential Evolution (DE) and (SAPS) algorithm & Benchmark test functions and SAT problem & Mean best fitness for DE and time-to-convergence (for SAPS) \\ \hline ParamILS, I-Race, and & & Benchmark test functions, Randomly selected Euclidean & Number of evaluations to the optimum for SGA, Distance to optimum for ACOTSP, and coloring problems. & Minimum speed for Spear \\ \hline \end{tabular}
\end{table}
Table 4: \(Cont.\)