A genetic cascade-correlation learning algorithm

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