## 1 Introduction
It is up to the designer which algorithm to apply and in which form (algorithm settings). There is generally no limit to algorithm combinations. In [9], the authors combined genetic algorithms with simulated annealing, while both of these algorithms are considered to be global-search strategies. The minimization of the compensation error of the magnetometer was addressed in their study, and the combination of population-based GA with single-individual SA helped in the achievement of this goal. The review of hybrid evolutionary optimization [10] shows that the hybrid approach suffers from similar problems as pure GAs. Finding the efficient population size, along with the proper tuning of genetic operators, is key. The authors pointed out that the mutation parameter seems to change its role in the hybrid approach (compared to solely GA approach), as the purpose of the GA is shifted toward exploration; therefore, the role of mutation should be to steer the algorithm from one basis to another, by adjusting its higher probability. Other hybridization options were highlighted, i.e., pairing the GA with an additional algorithm assuring the feasibility of solutions. In the case of LSM-based topology optimization, this may be achieved by the additional, one-variable optimization of the threshold parameter. However, this method could increase the final optimization time when the univariable optimization is carried out on a very nonlinear and multimodal landscape. In real-world industrial conditions, the time or computational effort available for optimization is limited. In these cases, finding the actual optimum may not be possible; therefore, the designers' task can be reformulated into finding the best quasi-optimum in the given time or using the available computational resources. Then, having an algorithm with well-tuned parameters is beneficial and desired, as presented, e.g., in [11; 12]. How to obtain the proper parameter values is one of the engineering challenges, especially when the solution requires time-consuming FEM-based simulation. In these cases, it is beneficial to use the surrogate modeling approach to replace costly numerical simulations with quick metamodel calculations. This approach is the aim of the authors' research and will be explained in detail in further chapters. Surrogate modeling, also referred to as metamodeling, is a constantly growing field of science. It allows not only for the replacement of the underlying, costly simulations and physical experiments, but it may also give some insight into the properties of the simulated or tested process, especially when dealing with normalized values, as the coefficient of the metamodels (as the polynomial regression model) represent the sensitivity or importance of particular terms or parameters [13]. Recent review papers [14; 15] show that the majority of the literature studies involve three types of metamodels: The artificial neural networks (ANNs), the Kriging, and the polynomial regression, which paired with statistical analysis is referred to as the response surface method (RSM). These surrogates account for 24, 15, and 17% of all surrogates in the recent papers. Moreover, the combination of metamodeling, along with hybrid optimization, is observed as an increasingly common practice among researchers. All of the aforementioned models were proposed in the XXth century; however, the recent development is focused, e.g., on reducing the number of hyper-parameters needed to be optimized during the Kriging fitting process or architecture of the ANNs. More complex Kriging models, such as the Universal Kriging incorporating the trend model, even though more sophisticated, do not always bring significant improvement due to limitations associated with the response of the underlying regression model (the trend).