Dynamic optimization by evolutionary algorithms applied to financial time series

Authors:
K. Yaniasaki;K. Kitakaze;M. Sekiguchi
Affiliations:
Tokyo Univ. of Inf. Sci., Chiba, Japan;Tokyo Univ. of Inf. Sci., Chiba, Japan;Tokyo Univ. of Inf. Sci., Chiba, Japan
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Abstract

It is not clear what is an optimum state, when it's objective function changes. Dynamic optimization contains trade-offs of which a good optimization at present may make it difficult to optimize at the next time after the objective function changed. This means a similarity between a dynamic optimization and a multiobjective optimization. So, in our previous works, we developed a method that uses multiobjective ranking to dynamic optimization problems. In this work we apply our proposed method to financial time series.