view:41599 Last Update: 2018-7-15
A. R. Khanteymoori, M. B. Menhaj and M. M. Homayounpour Structure Learning of Bayesian Networks Using Asexual Reproduction Optimization Structure Learning of Bayesian Networks Using Asexual Reproduction Optimization
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A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered 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 simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA. |