Pete Duell

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— The EENCL algorithm [1] has been proposed as a method for designing neural network ensembles for classification tasks, combining global evolution with a local search based on gradient descent. Two mechanisms encourage diversity: Negative Correlation Learning (NCL) and implicit fitness sharing. In order to better understand the success of EENCL, this work(More)
The EENCL algorithm [1] automatically designs neural network ensembles for classification, combining global evolution with local search based on gradient descent. Two mechanisms encourage diversity: Negative Correlation Learning (NCL) and implicit fitness sharing. This paper analyses EENCL, finding that NCL is not an essential component of the algorithm,(More)
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