A flexible neural network (FNN) is a multilayer feedforward neural network with the characteristics of: (1) overlayer connections; (2) variable activation functions for different nodes and (3) sparse connec- tions between the nodes. A new approach for designing the FNN based on neural tree encoding is proposed in this paper. The approach em- ploys the ant programming (AP) to evolve the architecture of the FNN and the particle swarm optimization (PSO) to optimize the parameters encoded in the neural tree. The performance and effectiveness of the pro- posed method are evaluated using time series prediction problems and compared with the related methods.
['Yuehui Chen', 'Bo Yang', 'Jiwen Dong']
Evolving Flexible Neural Networks Using Ant Programming and PSO Algorithm