Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An improved acceptance procedure for the hybrid Monte Carlo algorithm
Journal of Computational Physics
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian inference based on stationary fokker-planck sampling
Neural Computation
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
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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.