A New Evolutionary Algorithm for Structure Learning in Bayesian Networks
Publish place: 14th annual International CSI Computer Conference
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,876
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
CSICC14_084
تاریخ نمایه سازی: 24 خرداد 1388
Abstract:
A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this paper. 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 proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation. Results of simulation show that ARO outperforms GA because ARO results good structure in comparison with GA and the speed of convergence in ARO is more than GA. Finally, the ARO performance is statistically shown
Authors
A.R Khanteymoori
AmirKabir University, Computer Engineering Department, Tehran, Iran.
M.M Homayounpour
AmirKabir University, Computer Engineering Department, Tehran, Iran