Multiagent systems and societies of agents
Multiagent systems
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Hybrid Intelligent Systems for Stock Market Analysis
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Computational Intelligence: Principles, Techniques and Applications
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Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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International Journal of Intelligent Systems
A multi-agent system for analyzing the effect of information on prediction markets
International Journal of Intelligent Systems
Expert Systems with Applications: An International Journal
Evaluation of environmental impact upon human health with DeciMaS framework
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid evolutionary approach for solving the ontology alignment problem
International Journal of Intelligent Systems
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Stock price prediction is an important task for most investors and professional analysts. However, it is a tough problem because of the uncertainties involved in prices. This paper presents a four-layer fuzzy multiagent system (FMAS) architecture to develop a hybrid artificial intelligence model based on the coordination of intelligent agents performing data preprocessing and function approximation tasks for next-day stock price prediction. The first layer is dedicated to metadata creation. The second layer is aimed at data preprocessing using stepwise regression analysis and self-organizing map neural network clustering for modularizing prediction problems. The third layer is aimed at model building for each cluster using genetic fuzzy systems and evaluating built models to choose the best evolved fuzzy system for each cluster. Finally, the fourth layer provides model analysis and knowledge presentation. The capability of FMAS is evaluated by applying it on stock price data gathered from IT and airline sectors and comparing the outcomes with the results of other methods. The results show that FMAS outperforms all previous methods, so it can be considered as a suitable tool for stock price prediction problems. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.