Forecasting is a process that must be accurate, reliable, and fast, if it is applied on a sort-term or on-line basis. It plays a significant role in decision-making, enabling the overcoming of economic and operational problems. Traditional methods are proven effective when applied to linear or stationary assumptions. However, recent challenges are non-linear, high-dimensional and include more noise, requiring more complex methods. In order to overcome such a complexity, a hybrid approach, combining the Multimodel Partitioning Filter (MMPF) and a Genetic Algorithm for Resource Allocation (GARA), will be presented. The method aims to refine the initial probabilities (weights) provided by the MMPF through an iterative, fitness-driven search for optimal weight values. The comparison will be made between the proposed method and one previously presented that combined MMPF with Support Vector Machines (SVM).