### State-of-the-Art
Initialization procedures for local search algorithms may include random seeding or an accurate seeding based on the estimation of the demand points density [38]. However, multiple launches of simple local search algorithms from various randomly generated solutions do not provide a solution to a problem which is close to the global optimum. More advanced algorithms allow us to obtain the objective function values many times better than the local search methods [52]. Evolutionary algorithms are a class of search algorithms that are often used as function optimizers for static objective functions. Using specific evolutionary operators, the evolutionary algorithms recombine and modify a set (population) of candidate solutions to a problem. Various efficient evolutionary algorithms were developed for almost all types of optimization problems including machine learning problems. The use of the genetic and other evolutionary algorithms for solving discrete and continuous location problems [55; 56; 57; 58; 59; 60] is also a popular approach. For example, in [61], the authors proposed the online-learning-based reference vector evolutionary many-objective algorithm guided by the reference-vector-based decomposition strategy which employs a learning-based technology to enhance its generalization capability. This strategy employs a learning automaton to obtain the feature of the problem and the searching state according to the feedback information from the environment to adjust the mutation strategy for each sub-problem in different status situations and enhance the optimization performance. The authors use clustering to form groups of reference vectors with the same mutation strategy. The work [62] focuses on a discrete optimization problem of improving the seaside operations at marine container terminals. The authors proposed a new Adaptive Island Evolutionary Algorithm for the berth scheduling problem, aiming to minimize the total weighted service cost of vessels. The developed algorithm simultaneously executes separate evolutionary algorithms in parallel on its \"islands\" (subpopulations of solutions) and exchanges individuals between the \"islands\" based on an adaptive mechanism, which enables more efficient exploration of the problem search space. The authors of [63] proposed an alternative many-objective EA, called AnD. In evolutionary many-objective optimization, there exists a problem of selecting some promising individuals from the population. The AnD uses two strategies: angle-based selection and shift-based density estimation, which are employed to remove poor individuals one by one. In a pair of individuals with the minimum vector angle (most similar search directions), one of the individuals is considered as a candidate for elimination. In addition, the EA is a popular solution methodology, showing a promising performance in solving different types of vehicle routing problems [64; 65]. In [66], the authors presented an efficient EA developed to solve the mathematical model, which addresses the vehicle routing problem with a \"factory-in-a-box\". In [67], the authors proposed an approach to distinguish between bacterial and viral meningitis using genetic programming and decision trees able to determine the typology of meningitis for any combination of clinical parameters by achieving 100% of sensitivity. The EAs are popular for training in various the machine learning methods. In [68], the Salp Swam Algorithm (SSA) was deployed in training the Multilayer Perceptron (MLP) for data classification. The proposed method shows supremacy in results as compared with other evolutionary algorithm-based machine learning problems. In the case of p-median and similar location problems, evolutionary algorithms recombine the initial solution obtained by the ALA procedure or a local search algorithm. In genetic algorithms, a set (population) of solutions is successively improved with the use special genetic operators (algorithms) of initialization, selection, crossover, and mutation.