Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
An experimental evaluation of neural networks for classification
Computers and Operations Research
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Argumentation based decision making for autonomous agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Argumentation in Multi-Agent Systems: First International Workshop, ArgMAS 2004, New York, NY, USA, July 19, 2004, Revised Selected and Invited Papers (Lecture Notes in Computer Science)
A Tool for Portfolio Generation Using an Argumentation Based Decision Making Framework
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Fast learning in networks of locally-tuned processing units
Neural Computation
An argumentation framework for merging conflicting knowledge bases: the prioritized case
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Genetically determined variable structure multiple model estimation
IEEE Transactions on Signal Processing
Dynamic index tracking via multi-objective evolutionary algorithm
Applied Soft Computing
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In the current contribution, an application for constructing mutual fund portfolios is presented. This approach comprises several Intelligent Methods, namely an argumentation based decision making framework and a hybrid evolutionary forecasting algorithm which combines Genetic Algorithms (GA), MultiModel Partitioning (MMP) theory and Extended Kalman Filters (EKF). Specifically, the argumentation framework is employed in order to develop mutual funds performance models and select a small set of mutual funds, which will compose the final portfolio. On the other hand, the hybrid evolutionary forecasting algorithm is applied in order to forecast the market status (inflating or deflating) for the next investment period. The knowledge engineering approach and application development steps are also presented and discussed.