genetic algorithm optimization

In this paper an optimal portfolio selection is obtained so that it provides the maximal yield and at the same time satisfies the constraints on the value at risk. Value at risk is an important measure of extent to which a given portfolio is subject to different kinds of risk present in financial markets. The optimal weights of each share have been obtained using Genetic Algorithm (Gas). Actually GAs are stochastic parallel global–search algorithms based on the mechanism of natural genetics and the biological theory of evolution. Because GAs exploit strategies of genetic information and survival of the fittest to guide their search, they need not calculate the gradient or assume that the search space is differentiable or continuous. GAs simultaneously evaluate many points in the parameter space, so they are more likely to converge toward a global solution. Gas are very suitable for searching discrete, noisy, multimodal and complex space. The portfolio which is considered in this article has been selected from 12 various companies in the Tehran stock exchange. Simulation results show that the high performance of the VaR approach risk modeling and GA optimization method to selection an optimal portfolio under a pre-specified constraint on the value at risk.