The design and development of hybrid intelligent systems such as financial investment planning are difficult because they have a large number of components that have many interactions. Existing software development techniques cannot manage those complex interactions efficiently as those interactions may occur at unpredictable times, for unpredictable reasons, between unpredictable components. In this paper, we employed fussy algorithms, genetic algorithms, etc. to solve complicated financial portfolio management. The system starts with the financial risk tolerance evaluation based on fussy algorithms. Asset allocation, portfolio selections, interest predictions, and ordered weighted averaging can be conducted by using hybrid intelligent techniques. The planning agent in the system can easily access all intelligent processing agents, including financial risk tolerance assessment agent, asset allocation agent, portfolio selection agents, interest prediction agents, and decision aggregation agent. Overall system robustness is facilitated.
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