Using evolutionary neural networks for arterial waveform discrimination

Summary form only given, as follows. The authors discuss evolutionary programming and details experiments using evolutionary neural networks to discriminate between valid and nonphysiologically realizable arterial waveforms as observed during a surgical procedure. Evolutionary programming has been suggested for optimizing the weights and bias terms of neural networks during supervised learning. This technique effectively avoids the tendency to become stalled in locally optimal solutions, as is common with backpropagation and other gradient-based methods. The technique differs from common `genetic´ approaches in that there is no reliance on specific mutation operations, and the representation for the solution need not be a binary string. The use of evolutionary programming reduces the required complexity of the discriminating network, allowing for more robust performance