The Application Of The Genetic Algorithm And Quadratic Programming Portfolio Optimization

Portfolio optimization problem is an NP problem, and it is very difficult to reach the global optimum in a common method. As an efficient parallel global optimization search method, the genetic algorithm has been applied in many areas. By introducing genetic algorithm into the field of securities investment analysis and analyzing the sequential quadratic programming, this article calculates the ratio of different investment risk coefficient in case that yield remains constant. After a comparative analysis between the above result and the result calculated from the sequential quadratic programming, this article finds that the risk co-efficient of the former is less than the latter. At last, this article gets a conclusion that genetic algorithm is superior to sequential quadratic programming under some circumstances.