This research shows the portfolios optimization using micro genetic algorithms, to resolve the Markowitz's selecting investments model like a multi-objetive optimization, where is maximized profitalility and minization, where is maximized profitability and minimizing the risk, thus create a negotiation between the risk, thus create a negotiation between the two objectives, then find optimal solution. To solve this problem need a genetic algorithm for multi-objetive optimization, based Pareto's optimal. The results show that this application is more efficient than other similar processes(Non-dominated Sorting Genetic Algoritm II(NSGA II) and Pareto Archive Evolution Strategy (PAES)), but considering the period and the local market characteristics, its predictive power low.

This research shows the portfolios optimization using micro genetic algorithms, to resolve the Markowitz's selecting investments model like a multi-objetive optimization, where is maximized profitalility and minization, where is maximized profitability and minimizing the risk, thus create a negotiation between the risk, thus create a negotiation between the two objectives, then find optimal solution. To solve this problem need a genetic algorithm for multi-objetive optimization, based Pareto's optimal. The results show that this application is more efficient than other similar processes(Non-dominated Sorting Genetic Algoritm II(NSGA II) and Pareto Archive Evolution Strategy (PAES)), but considering the period and the local market characteristics, its predictive power low.