Evolutionary optimization in dynamic environments: Bringing the strengths of dynamic bayesian networks into bayesian optimization algorithm

Abstract

In this paper, a new evolutionary algorithm termed DBN-MBOA (Memory-based BOA with Dynamic Bayesian Networks) is proposed for the dynamic optimization. In DBN-MBOA, the knowledge obtained from previously solved problems is encoded in some structures called network translators. The network translators defined on non-stationary Dynamic Bayesian Networks (nsDBNs) describe the correlation between conditional dependencies of candidate solution variables before and after environmental changes. The network translators constructed for the changes are stored in memory. When any change occurs in the environment, a relevant network translator is retrieved from the memory and is used for modifying the dependencies of the current Bayesian network. In the retrieve stage, unlike existing memory-based methods, the relevant network translator is selected based on the characteristic of the change itself, not that of the new environmental state. Experimental results show that DBN-MBOA achieves better performance in random environments as well as cyclic environments. © 2013 ICIC International.