Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Start-Up Time Modeling in Combined Cycle Power Plants


This paper presents a hybrid approach based on soft computing techniques in order to estimate ambient temperature for those places where such datum is not available. Indeed, we combine the Back-Propagation (BP) algorithm and the Simple Genetic Algorithm (GA) in order to effectively train neural networks in such a way that the BP algorithm initialises a few individuals of the GA’s population. Experiments have been performed over all the available Italian places and results have shown a remarkable improvement in accuracy compared to the single and traditional methods.

Applications of Evolutionary Computing, _pp 123-131__, Lecture Notes in Computer Science,