Scalability and Optimisation of a Committee of Agents Using Genetic Algorithm

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dc.creatorMarwala, T.
dc.creatorDe Wilde, P.
dc.creatorCorreia, L.
dc.creatorMariano, P.
dc.creatorRibeiro, R.
dc.creatorAbramov, V.
dc.creatorSzirbik, N.
dc.creatorGoossenaerts, J.
dc.date2007-05-12
dc.date.accessioned2026-07-07T08:01:08Z
dc.date.available2026-07-07T08:01:08Z
dc.descriptionA 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.
dc.description6 pages, In Proceedings of the 2001 International Symposia on Soft Computing and Intelligent Systems for Industry, Scotland
dc.identifierhttps://arxiv.org/abs/0705.1757
dc.identifierhttp://arxiv.org/abs/0705.1757
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128854
dc.subjectMultiagent Systems
dc.titleScalability and Optimisation of a Committee of Agents Using Genetic Algorithm
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