Application of Genetic Algorithms in Stock Market Simulation

AbstractDevelopment of stock market is affected by many factors. It is difficult to predict changes in prices of stocks because of many parameters in behavioral algorithms. There is also problem with learning soft-skills because of many variables. Application of genetic algorithms can help find suitable pre-set of behavioral patterns, functions and its parameters. In this paper we describe creation and implementation genetic algorithms to existing multi-agent simulation. This existing simulation provides basic model of simulation of stock market members behavior. The main goal of this article is describe how to implement genetic algorithm into this type of simulation. The main advantage of using genetic algorithms is dynamically created decision process or function of each agent. Article describes process of creating decision, simulating behavior of agents which decision algorithm was created by genetic programming. Next point is to show, how can be this implementation of genetic algorithms used in learning process of simulation