## 4 Discussion  
The estimation of the career success of UTEQ graduates used a genetic algorithm prediction. This GA generates optimal prediction solutions through graduate tracking variables or parameters and achieves operational efficiency through genetic operators (e.g., elite selection of successful graduates). It has a greater capacity to search for graduates in short execution times. Crossover and mutation of graduates allow for a better diversity of the graduate population with high chances of success. On the other hand, other studies used a GA to diversify the search and solve real-world optimization problems [125], such as dynamic energy-saving optimization [130], volunteer teacher transfer problems [138] and intelligent educational systems [124]. GA of success prediction is adaptive and modifiable in population number, genes, generations and probabilities of genetic operators. That allowed the calculation of optimal graduate weights with different populations and iterations. Similarly to other studies, the GA of temporal network analysis increases individuals and enables the calculation of denser weights, even by reducing the number of generations [139]. Likewise, the GA improves its performance through forwarding rules, where the more significant number of iterations represents better results [140]. In this study, the GA is reproducible and adaptable to the parameters of the UTEQ graduate tracking. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Alumni Tracking**} & **Career Success** & \\  & **(Genetic Algorithm Variables)** & **Relationship** \\ \hline \multirow{6}{*}{Product (knowledge, skills, career, motivation)} & Career & Career success depends on the knowledge acquired during the training process. \\ \cline{2-3}  & & Age & Age is a significant predictor in estimating professional success and a dynamic parameter contributing to alumni’s decision making. \\ \cline{2-3}  & Development of basic skills and competencies & \\ \cline{2-3}  & Job satisfaction & Subjective metrics for the occupational analysis of graduates. \\ \hline \multirow{6}{*}{Results (transition to employment, employment, consumption to society)} & Satisfaction with the knowledge and skills acquired in the higher education institution & Satisfaction with the knowledge acquired and skills development are complementary to professional development. \\ \cline{2-3}  & Employment & Employment measures career success, and it is an indicator of the professional results of universities. \\ \cline{2-3}  & Transition to employment (the year of getting a job and year of graduation) & The lack of a link between the labor market and the university influences the transition to employment. \\ \cline{1-1} \cline{2-3}  & Family income & Contributes to the socioeconomic analysis of graduates. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Relationship of career success with the alumni tracking. The prediction model with the highest reliability of the GA multidimensionally estimates the career success of graduates (Table 7); that is, it significantly relates objective and subjective variables in the prediction. Where professional satisfaction positively affects the time of transition to work, family income and development of skills of graduates. Similarly, other studies measure subjective career success in a multidimensional way through learning and development, work/life balance, financial security and entrepreneurship, considering these dimensions as significant predictors in estimating career success [90]. They also relate professional skills and employability [33], the quality of internships and the proactive personality [141] in measuring career success. On the contrary, other career success estimates consider a single variable's weight. For example, the dynamics of permanent learning mentality relate to objective and subjective career success [142], whereas age has a significant relationship with success [81].