NMIEDA: Estimation of distribution algorithm based on normalized mutual information

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.

Automated summary

The NMIEDA algorithm is proposed to overcome premature convergence in bivariate estimation algorithms by using normalized mutual information to measure variable interaction and generating a dependency forest model. It provides a new updating mechanism based on sporadic model building and a reward and punishment scheme, and adopts a new sampling mechanism to improve efficiency by combining stochastic sampling, opposition-based learning, and mutation operators. Simulation results show that NMIEDA outperforms other bivariate algorithms on benchmark and real-world problems.