Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm
Abstract
A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agents using genetic algorithms. The committees of agents buy and sell stocks by following this procedure: (1) obtain the current price of stocks; (2) predict the future price of stocks; (3) and for a given price trade until all the players are mutually satisfied. The trading of stocks is conducted by following these rules: (1) if a player expects an increase in price then it tries to buy the stock; (2) else if it expects a drop in the price, it sells the stock; (3)and the order in which a player participates in the game is random. The proposed procedure is implemented to simulate trading of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear relationship between the number of players and agents versus the computational time to run the complete simulation is observed. It is also found that no player has a monopolistic advantage.
Cite
@article{arxiv.0705.1757,
title = {Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm},
author = {T. Marwala and P. De Wilde and L. Correia and P. Mariano and R. Ribeiro and V. Abramov and N. Szirbik and J. Goossenaerts},
journal= {arXiv preprint arXiv:0705.1757},
year = {2007}
}