Hybrid genetic training of gated mixtures of experts for nonlinear time series forecasting

In this paper, we introduce a genetic algorithm-based training mechanism (HGT-GAME) toward the automatic structural design and parameter configuration of gated mixtures of experts (ME). In HGT-GAME, a whole ME instance is codified into a given chromosome. By employing regulatory genes, our approach enables the automatic pruning and growing of experts in a way to properly match the complexity of the task at hand. Moreover, to leverage HGT-GAME´s effectiveness a local search refinement upon each ME chromosome is performed in each generation via the gradient descent-learning algorithm. Forecasting experiments evaluate the performance of gated MEs trained with HGT-GAME.