Fast and Efficient Evolutionary Algorithms Based on Bayesian Networks

Abstract—Evolutionary algorithms are powerful search techniques which have been used successfully in many different domains. Parallel evolutionary algorithm has become a research focus due to its easy implement and promise substantial gains in performance. In this paper a framework of tree-modelbased parallel evolutionary algorithm (T-PEA) is proposed. The presented method employs Bayesian Dirichlet metric to construct a tree model from a set of potential solutions, which is then used to model potential solutions and guide exploration in the search space. The correctness and rationality of the proposed method for learning tree models are analyzed and proved in the context of genetic and evolutionary. The method is important not only for T-PEA, but also for machine learning and data mining. The experimental results show that the proposed algorithm can efficiently and rapidly converge and obtain the optimal solution from all test functions.