Dynamic optimization by evolutionary algorithms applied to financial time series

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