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NMIEDA: Estimation of distribution algorithm based on normalized mutual information
Author(s) -
Lin Zhiyi,
Su Qing,
Xie Guobo
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6074
Subject(s) - mutual information , estimation of distribution algorithm , convergence (economics) , benchmark (surveying) , dependency (uml) , bivariate analysis , algorithm , computer science , sampling (signal processing) , mathematical optimization , mathematics , data mining , artificial intelligence , machine learning , geodesy , filter (signal processing) , geography , economics , computer vision , economic growth
Summary A new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two variables and then generate a dependency forest model. Second, based on the concept of sporadic model building and a reward and punishment scheme in Selfish Gene, NMIEDA provides a new updating mechanism that accelerates the convergence speed. Finally, a new sampling mechanism is adopted in NMIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition‐based learning scheme and the mutation operator. The simulation results on benchmark problems and real‐world problems demonstrate that NMIEDA often outperforms several other bivariate algorithms.