TY - JOUR ID - 40 TI -  Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization JO - AUT Journal of Electrical Engineering JA - EEJ LA - en SN - 2588-2910 AU - Khanteymoori, R. AU - Homayounpour, M. M. AU - Menhaj, M. B. AD - Corresponding Author, A. R. Khanteymoori is with the Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran (e-mail: khanteymoori@aut.ac.ir). AD - M. M. Homayounpour is with the Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran (e-mail: hamayoun@aut.ac.ir). AD - M. B. Menhaj is with the Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran (e-mail: mb.menhaj@aut.ac.ir). Y1 - 2012 PY - 2012 VL - 44 IS - 1 SP - 43 EP - 53 KW - Bayesian networks KW - Structure Learning KW - Evolutionary algorithms KW - Genetic algorithms DO - 10.22060/eej.2012.40 N2 - A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this letter. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulation show that ARO outperforms GA because ARO results in good structure and fast convergence rate in comparison with GA. UR - https://eej.aut.ac.ir/article_40.html L1 - https://eej.aut.ac.ir/article_40_277149fec9ff54681c54e65da4e80547.pdf ER -