## 2 Related Works  
In this section, we present some proposals in connection with fitness landscape analysis. Examination of the optimization space is a useful component in analyzing the general behavior of the problem domain. The structure of the optimization space influences the efficiency of convergence, such as the quality of the obtained global or local optimum. Examining the fitness space can be useful for selecting the appropriate optimization methods (which operators are worth using for heuristic algorithms). One benefit of the presented methods is that it can be used as the foundation for further formal theoretical analyzes,there is no need to perform expensive experiments (such as when comparing the results of metaheuristic algorithms with test results). Our analyzes include Vehicle Routing Problem [19; 20], Traveling Salesman Problem [21; 22; 23], Job Scheduling Problem [21], Test Function [24], Quadratic Assignment Problem [25]. The authors of [19] perform a fitness landscape analysis of the Vehicle Routing Problem. Information is analyzed from a theoretical perspective, and the operators of the Genetic Algorithm (Swap, Inversion, Insertion, Displacement, Partially Matched Crossover--PMX, Uniform Order Crossover--UOX, Cycle Crossover--CX) were chosen as the subject of their analysis. The concepts of information content, partial information content, and density-basin information are reported. The authors of [20] also perform a fitness landscape analysis of the vehicle routing problem. After presenting the mathematical model, the authors review the concepts of search space (fitness landscape). Then, in proportion to the fitness values, the averages of the distances taken from the solutions are illustrated and the distances taken from the best solution are also plotted. The authors of [22] focus on the analysis of the Memetic Algorithm in solving the Traveling Salesman Problem. After discussing each concept of fitness landscape, the authors also plot each solution in a coordinate system according to fitness values. The subject of the analysis is the distance from the optimum and the differences in fitness. Analyses are performed on benchmark data sets. The Traveling Salesman Problem and the No-Wait Job Scheduling are discussed by the authors in [21]. Fitness landscape analysis is performed as autocorrelation, time to local optimum, number of local optimums, its increase as the problem increases, distances from the optimum, the probability that we will reach the optimum. An example of the analysis of benchmark continuous functions can also be found in the literature, for example, in [24] the Differential Evolution Algorithm fitness landscape analysis is presented. Presentations of the most important concepts of fitness landscape: fitness distance correlation, correlation length, epistasis, evolvability and neutrality. The authors focused on a continuous optimization task. Benchmark functions were solved such as Sphere function, Rosenbrock function, Step function, Schwefel function, Rastrigin function, Griewangk function, Ackley function, Easom function, Schwefel's Ridge function. Schwefel function, the Easom function, the Schwefel's Ridge function had a small fitness distance correlation, which means, that a Hill Climbing algorithm can easily solve these problems. The analysis of the Traveling Salesman Problem with benchmark data sets is also presented in [23], by comparing the Iterated Local Search and Hill Climbing Algorithm. In this article, experiments were executed on benchmark data sets. They applied 3 TSP-lib instances for the simulations. The distance and running time compared to the best result known so far are compared. An example of the analysis of the Quadratic Assignment Problem (QAP) fitness landscape can also be found in [25]. In addition to presenting the task, operators are also presented as pairwise exchange or swap. In addition, the basin of attraction, the local optima network, the random walk autocorrelation function, and the autocorrelation length are also detailed.