Open AccessStructure Learning in Bayesian Networks Using Asexual Reproduction Optimization
Author(s) -
Khanteymoori Ali Reza,
Menhaj Mohammad Bagher,
Homayounpour Mohammad Mehdi
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.0110.0114
Subject(s) - asexual reproduction , benchmark (surveying) , convergence (economics) , computer science , reproduction , bayesian optimization , artificial intelligence , evolutionary algorithm , rate of convergence , bayesian network , bayesian probability , mathematical optimization , machine learning , mathematics , key (lock) , biology , ecology , computer security , geodesy , geography , economics , economic growth
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.