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The literature on trading algorithms based on Grammatical Evolution commonly presents solutions that rely on static approaches. Given the prevalence of structural change in financial time series, that implies that the rules might have to be updated at predefined time intervals. We introduce an alternative solution based on an ensemble of models which are trained using a sliding window. The structure of the ensemble combines the flexibility required to adapt to structural changes with the need to control for the excessive transaction costs associated with over-trading. The performance of the algorithm is benchmarked against five different comparable strategies that include the traditional static approach, the generation of trading rules that are used for single time period and are subsequently discarded, and three alternatives based on ensembles with different voting schemes. The experimental results, based on market data, show that the suggested approach offers very competitive results against comparable solutions and highlight the importance of containing transaction costs.