## 4 Technical Implementation  
As a key part of this study, the technical implementation of the previously introduced mathematical formulation for optimizing the bolted end-plate connection is detailed, leading to the development of a comprehensive automated design framework. This is achieved by developing a standalone PYTHON-based program that incorporates a genetic algorithm (GA) and operates in conjunction with the ABAQUS finite element software during the optimization process. The connection with ABAQUS is established through the ABAQUS Scripting Interface [36], which enables the PYTHON code to automatically create finite element models, submit analyses, and extract relevant results from the output files. The reason behind this choice lies in the need to accommodate complex, discrete, and rule-based design variables, which required greater flexibility than what is offered by standard optimization workflows within ABAQUS. Before detailing the implementation, a brief introduction to the GA methodology is provided, highlighting its key components. GA is a versatile heuristic optimization technique, known for its adaptability in addressing a wide range of real-world engineering problems [31]. Its core concept involves generating a population of candidate solutions, with each individual representing a potential solution to the optimization problem. The algorithm simulates the process of natural selection to iteratively improve these candidates by maximizing or minimizing the fitness function through many generations [32]. This function evaluates the quality of each solution and plays a central role in guiding the evolutionary search, which is driven by genetic operators such as selection, crossover, and mutation. In the general form of GA, the characteristics of individuals are encoded into bit-string-based chromosomes, which are also employed in this study. The process begins with the random generation of an initial population within a predefined range, referred to as the population size. The fitness of each individual is then evaluated. Next, the selection operator is applied to identify parent solutions for the next generation. Crossover is subsequently performed to combine the genetic information of selected individuals, producing new offspring. Finally, mutation is applied to introduce random alterations in the chromosomes, with the extent of variation controlled by the mutation rate. The developed GA-based framework integrates several well-established evolutionary operators. Tournament selection is employed to construct the mating pool by randomly selecting a subset of individuals--defined by the tournament size--from the population, with the individual exhibiting the best fitness value advancing [32]. Uniform crossover is used to generate offspring by randomly exchanging genes between two parent chromosomes, guided by a specified crossover probability [34]. To maintain genetic diversity, a dynamically adjusted mutation rate introduces random alterations in the offspring. Furthermore, an elitism strategy is implemented to ensure that the best-performing individual of each generation is directly carried over to the next, thereby preserving high-quality solutions throughout the optimization process. The key GA parameters used in this study for the initial optimization process are summarized in Table 1.  
Reflecting the principles of GA, the entire design process is illustrated in Figure 3. Prior to initiating the optimization, the design domain must be defined based on the specifics of the given case. Subsequently, the initial population is generated. For further clarification, the binary-based chromosomes are randomly created within the predefined design space. These chromosomes are then decoded into their corresponding physical parameters, and the associated finite element model is automatically generated using the ABAQUS Scripting Interface. Once the structural configuration is created, the fitness evaluation is carried out in an automated manner.