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- W3194008367 abstract "Designing neural networks for classification or regression can be considered a search problem, and, as such, can be approached using different optimization procedures, all of them with several design challenges: The first and more important is to constrain the search space in such a way that proper solutions can be found in a reasonable amount of time; the second is to take into account that, depending on how the optimization procedure is formulated, the fitness score used for it can have a certain degree of uncertainty. This means that creating a framework for evolving neural networks for classification implies taking a series of decisions that range from the purely technical to the algorithmic at different levels: neural or the optimization framework chosen. This will be the focus of this paper, where we will introduce DeepGProp, a framework for genetic optimization of multilayer perceptrons that efficiently explores space of neural nets with different layers and layer size." @default.
- W3194008367 created "2021-08-30" @default.
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- W3194008367 date "2021-01-01" @default.
- W3194008367 modified "2023-09-23" @default.
- W3194008367 title "EvoMLP: A Framework for Evolving Multilayer Perceptrons" @default.
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- W3194008367 doi "https://doi.org/10.1007/978-3-030-85099-9_27" @default.
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