Stochastic portfolio model and its application for genetic algorithms

Genetic algorithms (GAs) are stochastic search techniques based on the mechanics of natural selection and natural genetics. By using genetic operators and cumulative information, genetic algorithms prone the search space and generate a set of plausible solutions. In this paper, the adaptive genetic algorithms are applied to solve the portfolio investment problem in which expected return rates are stochastic variables. First, the stochastic model of portfolio and reliable decision are presented. Second, the adaptive genetic algorithm to solve reliable model is given. Finally, a numerical example of a portfolio selection problem is given to illustrate our proposed effective means.