Bayesian training of neural networks using genetic programming

Bayesian neural network trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. The algorithm proposed here has the ability to learn using samples obtained from previous steps merged using concepts of natural evolution which include mutation, crossover and reproduction. The reproduction function is the Metropolis framework and binary mutation as well as simple crossover, are also used. The proposed algorithm is tested on simulated function, an artificial taster using measured data as well as condition monitoring of structures and the results are compared to those of a classical MCMC method. Results confirm that Bayesian neural networks trained using genetic programming offers better performance and efficiency than the classical approach.