EAs are biologically inspired metaheuristics, where the candidate solutions to the problem of interest are encoded in the chromosomes [20; 21]. At the beginning, a typical EA initializes the population, represented by a group of chromosomes, and evaluates fitness (i.e., quality of the solutions) of the initial population chromosomes. After that, the EA starts an iterative procedure, where the chromosomes are continuously changed by applying specific EA operators (i.e., parent selection, crossover, mutation, offspring selection) with the main objective to discover superior solutions. The iterative procedure is terminated once the EA satisfies a specific convergence criterion. Each EA has several parameters, including population size (i.e., the number of chromosomes in the population), crossover probability, mutation probability, selection operator parameters, and others. There are two approaches for selection of the EA parameters [20; 21; 22], including the following (see Figure 1): (1) parameter tuning; and (2) parameter control. Note that the aforementioned approaches can be used to set parameters not only for EAs, but also for the other heuristic and metaheuristic algorithms. As for the parameter tuning approach, different values of each parameter are evaluated for a given EA, and the best parameter values are selected based on the analysis of a tradeoff between the objective function and computational time values (the latter analysis is referred to as the \"parameter tuning\" analysis). The parameter tuning approach assumes that the selected parameter values remain unchanged throughout the algorithmic run. On the other hand, the parameter control approach adjusts the values of EA parameters throughout the algorithmic run based on certain strategies. There are three common types of the parameter control strategies [20; 21; 22], including the following (see Figure 1): (1) deterministic--the algorithmic parameters are altered based on a certain counter (e.g., computational time, number of generations) without any feedback from the search; (2) adaptive--the algorithmic parameters are altered based on feedback from the search (e.g., behavior of the objective/fitness function);and (3) self-adaptive--the algorithmic parameters are encoded in the chromosomes and evolve throughout the algorithmic run. The EAs, presented in the BSP literature, primarily rely on the parameter tuning strategy. In some of the recent BSP studies, the deterministic and adaptive parameter control strategies were implemented within the proposed EAs [10; 12]. Both deterministic and adaptive parameter control strategies were found to be promising and outperformed the parameter tuning strategy. Specifically, the EAs with parameter control can more efficiently move along the search space, identify promising domains of the search space, and exploit the identified domains for superior solutions [10; 12]. On the other hand, setting constant parameter values, which do not change throughout the algorithmic run (i.e., the parameter tuning strategy), typically limits the explorative and exploitative EA capabilities. Although the self-adaptive parameter control strategy has been used in the EA literature [20; 21; 22], none of the published to date studies applied such parameter control strategy for the BSP. The contributions of this study to the state-of-the-art can be summarized as follows:  
1. A novel self-adaptive EA is proposed for the BSP, where the crossover and mutation probabilities are encoded in the chromosomes and evolve throughout the algorithmic run;
2. A novel heuristic is presented for the population initialization, which accounts for the spatial requirements in berth scheduling;
3. A comprehensive comparative analysis is conducted to evaluate the EA with self-adaptive parameter control strategy against the alternative EAs, which rely on the deterministic parameter control, adaptive parameter control, and parameter tuning strategies, respectively; and
4.