# A Literature Survey on Offline Automatic Algorithm Configuration
## 6 Multi-Objective META-EAs
#### 2.1.2 ParamILS  
ParamILS, another model-free method, suggested by [46] and improved by [47], is an iterated local search algorithm, which is influenced by the hand-made algorithm configuration methods. The ParamILS process begins with a default parameter value generally based on user experience with randomly chosen r configurations from the dedicated configuration space, and the performance of the algorithm is improved iteratively by searching in its neighborhood, which can be described as changing only one parameter. In other words, an iterative first-improvement algorithm is a local search procedure of ParamILS. These selected (r + 1) configurations are evaluated and the local search process is initialized with the best performing configuration. The ParamILS builds a chain of local optima by iterating the following steps. Starting from the initial solution, it performs s random perturbation to avoid local optima, which are implemented via changing the values of a few parameters to randomly chosen values. Then a local search process is performed and a comparison is executed between the newly obtained candidate configuration and the previous best one to decide whether to keep or reject the new one. According to this comparison, if an improvement on the best solution is observed, the new one is selected as the best configuration. Additionally, ParamILS also includes a diversification mechanism with a re-start probability (p\({}_{\text{r}}\)). After each local search, ParamILS is restarted using a random initial configuration. It stops when the allowed number of executions or time limit is achieved and, it gives one best performing configuration as an output. In their empirical study, for all tuning scenarios, parameter configurations found by ParamILS always demonstrated better performance than the default parameter values and the CALIBRA system [48]. Two different versions of ParamILS (available at [[http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/](http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/)]([http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/](http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/)) (accessed on 10 June 2022)) are suggested, such as BasicILS and FocusedILS. Their differences are in the way of defining one configuration as better than another one. BasicILS uses a fixed number of random seeds or (problem instance) for performance comparison among the configurations, while FocusedILS uses the dominance concept. Essentially, one configuration dominates the other (i.e., c\({}_{1}\) dominates c\({}_{2}\) configuration) if and only if the mean performance of the algorithm with c\({}_{1}\) on m\({}_{1}\) seed is better than c\({}_{2}\) on m\({}_{2}\) seeds when m\({}_{1}\) > m\({}_{2}\). In other terms, when FocusedILS finds that configuration c\({}_{1}\) performs better than c\({}_{2}\), it executes additional runs (by increasing the number of seeds or instances to be solved) to prove that 'c\({}_{1}\) dominates c\({}_{2}\)'. Moreover, although over-tuning and over-confidence problems were already argued by [2], the first statistical arguments and experimental results are given in this study. For details of these problems and a detailed definition of a better mechanism, we refer to the original paper [46]. ParamILS method is improved by introducing a new \"_adaptive capping_\" mechanism [47]. The results of their experiments showed that adaptive capping accelerates both BasicILS and FocusedILS and can find well-performing parameter settings of complex and highly parameterized algorithms such as the commercial optimization tool CPLEX (available at [[http://www.ibm.com/software/integration/optimization/cplex-optimizer/](http://www.ibm.com/software/integration/optimization/cplex-optimizer/)]([http://www.ibm.com/software/integration/optimization/cplex-optimizer/](http://www.ibm.com/software/integration/optimization/cplex-optimizer/)) (accessed on 10 June 2022)).