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|Type:||Artigo de evento|
|Title:||Hybrid Genetic Training Of Gated Mixtures Of Experts For Nonlinear Time Series Forecasting|
Von Zuben F.J.
|Abstract:||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.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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