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- W2135486589 abstract "Gradient descent techniques such as backpropagation have been used effectively to train neural network connection weights; however, in some applications gradient information may not be available. Biologically inspired genetic algorithms provide an alternative. The paper explores an approach in which a traditional genetic algorithm using standard two-point crossover and mutation is applied within the cascade-correlation learning architecture to train neural network connection weights. In the cascade-correlation architecture the hidden unit feature detector mapping is static; therefore, the possibility of the crossover operator shifting genetic material out of its useful context is reduced. >" @default.
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- W2135486589 date "2003-01-02" @default.
- W2135486589 modified "2023-09-24" @default.
- W2135486589 title "A genetic cascade-correlation learning algorithm" @default.
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- W2135486589 doi "https://doi.org/10.1109/cogann.1992.273943" @default.
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