# A Literature Survey on Offline Automatic Algorithm Configuration
## 5 REVAC  
[PERSON] and [PERSON] [25; 26], suggested Relevance Estimation and Value Calibration of parameters (REVAC), and enhanced meta-EAs, to tune numerical parameters of optimization algorithms (continuous and categorical parameters needs to be discretized). The REVAC approach is based on EDA (Estimation of Distribution Algorithm [27]). Revac starts with the \"M\" configurations and at each iteration, the oldest configuration is changed with a newly created child parameter vector with the aid of multi-parent crossover and a mutation transformation. Information theory is used to estimate the expected performance when parameter values are chosen from a probability distribution (parameter relevance) instead of predicting the algorithm performance for different parameter configurations. For each tunable parameter, it gives one best value and one interval of values. It stops when the assessed number of execution is achieved. According to the Shannon entropy measurement of these distributions, a parameter that shows low entropy (which means the parameter value range is narrow) is considered more relevant to the algorithm performance. For a detailed discussion about REVAC's procedures details such as probability distributions and entropy, [28] is a good guide. In [19], a comparison of REVAC and Meta-GA was executed, and REVAC found better performing and robust configurations than those configured manually and by the meta-GA.  
[PERSON] et al. [29] executed a study about the effects of different design choices that underline the cost of tuning. The REVAC method was further enhanced in the study of [PERSON] and [PERSON] [30] by using an advanced technique of aggregating the performance of multiple runs. Another extension of REVAC was introduced by [PERSON] and [PERSON] [31] to find widely applicable parameter values. According to the no-free-lunch theorem [32], a parameter configuration that performs well on one kind of problem can perform worse on the other kind. [PERSON] and [PERSON] [31] executed the study in order to find good parameter values of a simple genetic algorithm for more than one widely used test function with REVAC tuning. So, they compared their generalist EA (whose parameters are tuned for a set of test functions) with a benchmark EA (whose parameter values are chosen by 'common wisdom') and with a specialist EA (which is a parameter set that shows good performance on one specific type of problem). The term \"generalist\" is used to demonstrate parameter values that perform well on a whole possible set of test problems, and this is infeasible according to the no-free-lunch theorem. Therefore, their claims turned out to be that generalist parameter values perform well on the previously defined set of test functions. They described this complete system in MOBAT [33] (available at [[http://mobat.sourceforge.net](http://mobat.sourceforge.net)]([http://mobat.sourceforge.net](http://mobat.sourceforge.net)), (accessed on 10 June 2022)). Results of the experiment demonstrated that REVAC is also able to find good parameter vectors for a set of test problems (not only for a single problem), which are different from the suggested values by common wisdom. They underline that the best generalist will always be linked with the definition of the generalist. Furthermore, this experiment demonstrated that several runs per parameter vector are critical, where excessive runs have high computational costs and too few runs lead to inaccurately estimated utility and inaccurate results. Additionally, they mentioned specific problems raised in a generalist configuration process based on the difference in difficulty levels of problems in the test suites, which cause a hidden bias. They suggested using a method from multi-objective optimization, aiming to establish the Pareto front to overcome these problems.