Abstract
Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian Optimization Algorithm with Random Immigration (BOARI). The algorithm generates and migrates random individuals as a way to improve the performance of the Bayesian Optimization Algorithm (BOA) by maintaining the genetic diversity of the population along the generations. The proposed approach has been evaluated and compared to BOA using benchmark functions. Results indicate that, with appropriate settings, the algorithm is able to achieve better solutions than the standard BOA for these functions.
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