کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6858942 1438435 2016 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Structural learning of Bayesian networks by bacterial foraging optimization
ترجمه فارسی عنوان
یادگیری ساختاری شبکه های بیزی توسط بهینه سازی باکتری های خوراکی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Algorithms inspired by swarm intelligence have been used for many optimization problems and their effectiveness has been proven in many fields. We propose a new swarm intelligence algorithm for structural learning of Bayesian networks, BFO-B, based on bacterial foraging optimization. In the BFO-B algorithm, each bacterium corresponds to a candidate solution that represents a Bayesian network structure, and the algorithm operates under three principal mechanisms: chemotaxis, reproduction, and elimination and dispersal. The chemotaxis mechanism uses four operators to randomly and greedily optimize each solution in a bacterial population, then the reproduction mechanism simulates survival of the fittest to exploit superior solutions and speed convergence of the optimization. Finally, an elimination and dispersal mechanism controls the exploration processes and jumps out of a local optima with a certain probability. We tested the individual contributions of four algorithm operators and compared with two state of the art swarm intelligence based algorithms and seven other well-known algorithms on many benchmark networks. The experimental results verify that the proposed BFO-B algorithm is a viable alternative to learn the structures of Bayesian networks, and is also highly competitive compared to state of the art algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 69, February 2016, Pages 147-167
نویسندگان
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