Unsupervised design of Artificial Neural Networks via multi-dimensional Particle Swarm Optimization

In this paper, we present a novel and efficient approach for automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. The evolution technique, the so-called multidimensional Particle Swarm Optimization (MD PSO) re-forms the native structure of PSO particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. So in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek for positional optimum in the error space and dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds ...

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: E. Alper Yildirim, Ince Turker, Moncef Gabbouj, Serkan Kiranyaz
• Keywords: Computer Vision, Dimensional Pso Process, ICPR 2008, MD PSO, MD PSO Process