## 4 Discussion
The GA of this study allows the use of various variables according to the follow-up of UTEQ graduates and determines the significance between them to predict its graduates' success better. Some studies estimate professional success through different approaches such as multivariate linear regression, logarithmic, multiple standard and hierarchical regression, analyzing objective predictor variables such as compensation, promotion, salary, profession, time dedicated to working, leadership, organizational commitment and gender, and subjective such as perceived success. These studies demonstrated an average influence of 25%, with a maximum of 51%, of predictor variables on objective or subjective career success outcomes [143; 144; 145; 146; 147]. The study proposed in this article used a prediction GA that estimated the professional success of 500 UTEQ graduates with 87.61% reliability, so the model's goodness of fit is acceptable with all its significant predictors considering the representative sample in different iterations. Similarly, other GAs make their estimates in various applications (e.g., meteorology, economics and health), reaching optimal results justified by the prediction accuracy. For example, rainfall prediction reported an accuracy of 80% in its estimates [148]. Likewise, a GA applied estimated financial problems in private sanitation companies, which reflected an accuracy of 85.16% [149]. Another study used a GA to optimize route transport for ambulances, where it detected an accuracy of 73.5% [150]. Some studies applied different methodologies to the GA, demonstrating the relationship of the achievement of their graduates through the analysis of parameters of the alumni program. For example, a study from a university in Israel used exploratory factor analysis to relate the development of general and specific skills of 21 st-century graduates and students to various teaching and learning methods [15]. Other studies applied descriptive statistics and multiple linear regression to determine the satisfaction of university graduates in Indonesia and the United States through the relationship between the variable infrastructure, teacher professionalism, employment opportunities, social classes and curricular relevance [11; 151]. On the other hand, universities in Norway and Turkey conducted studies on the performance analysis of graduates, using descriptive statistics to improve graduates' research skills, study plans, competencies, career development and quality of education [10; 12]. In addition, a graduate relationship management model used logistic regression to analyze the demographics, lifestyles, expectations and interests of graduates from a university in Thailand to establish marketing strategies for Alumni associations [18]. So, the GA of the present study is a proposal for measuring career success that includes the use of objective and subjective variables and their interaction with products and results of the alumni program. Furthermore, it contributes to the follow-up of its graduates and establishes improvements in education and educational management in higher education institutions. The main limitation of this research is the low percentage of responses by graduates in possible predictor variables such as the position of the graduate for the estimation of professional success.