Emergence of Optimal Bayesian Networks from Datasets WITHOUT Backtracking using an Evolutionary Algorithm

I.O. Osunmakinde and A. Potgieter (South Africa)


Computational Intelligence, Genetic Algorithms and Bayesian Networks


We propose a new Hybrid Genetic Algorithm (HGA) developed from the domain of evolutionary algorithms to evolve optimal Bayesian networks from datasets. For its learning process, it uses genetic operators engineered from information theoretic and mathematical fields including Mutual Information (MI), Extended Dependency Analysis (EDA), Mathematical Power Sets and Minimum Description Length (MDL). Unlike our HGA, existing genetic algorithms (GAs) use genetic operators that usually use backtracking, which is an overhead for a learning algorithm. In our research, we prevented backtracking using an inner-loop and carried out several evaluation experiments. Our empirical results and structural evaluations showed that a HGA can discover optimal networks from datasets that we selected from different domains.

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