%0 Journal Article %1 Zhang:2001:JSA %A Zhang, Byoung-Tak %A Cho, Dong-Yeon %D 2001 %J Journal of Systems Architecture %K Carlo Markov Monte System chain identification, %N 7 %P 587--599 %T System Identification Using Evolutionary Markov Chain Monte Carlo %V 47 %X System identification involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimises system architectures for the identification of unknown target systems. The method is distinguished from existing evolutionary algorithms in that the individuals are generated from a probability distribution as in Markov chain Monte Carlo (MCMC). It is also distinguished from conventional MCMC methods in that the search is population-based as in standard evolutionary algorithms. The effectiveness of this hybrid of evolutionary computation and MCMC is tested on a practical problem, i.e., evolving neural net architectures for the identification of nonlinear dynamic systems. Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.

@article{Zhang:2001:JSA, abstract = {System identification involves determination of the functional structure of a target system that underlies the observed data. In this paper, we present a probabilistic evolutionary method that optimises system architectures for the identification of unknown target systems. The method is distinguished from existing evolutionary algorithms in that the individuals are generated from a probability distribution as in Markov chain Monte Carlo (MCMC). It is also distinguished from conventional MCMC methods in that the search is population-based as in standard evolutionary algorithms. The effectiveness of this hybrid of evolutionary computation and MCMC is tested on a practical problem, i.e., evolving neural net architectures for the identification of nonlinear dynamic systems. Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Zhang, Byoung-Tak and Cho, Dong-Yeon}, biburl = {https://www.bibsonomy.org/bibtex/221261ef86b7523491a3e5ab6e04ccdee/brazovayeye}, interhash = {c8029b1c8c92b5555a1d8b950f88542a}, intrahash = {21261ef86b7523491a3e5ab6e04ccdee}, journal = {Journal of Systems Architecture}, keywords = {Carlo Markov Monte System chain identification,}, month = {July}, number = 7, pages = {587--599}, timestamp = {2008-06-19T17:55:24.000+0200}, title = {System Identification Using Evolutionary Markov Chain Monte Carlo}, volume = 47, year = 2001 }