A Novel Hybrid Optimization Method with Application in Cascade-Correlation Neural Network Training

In this paper, based on the fusion of the clonal selection algorithm (CSA) and differential evolution (DE) method, we propose a novel optimization scheme: CSA-DE. The DE is employed here to increase the affinities of the clones of the antibodies (Abs) in the CSA. Several nonlinear functions are used to verify and demonstrate the effectiveness of this hybrid optimization approach. It is further applied for the construction of the cascade-correlation (C-C) neural network, in which the optimal hidden nodes can be obtained.