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Survey on applications of biased-random key genetic algorithms for solving optimization problems

This paper presents a survey on studies devising biased-random key genetic algorithms (BRKGAs), a novel variant of the ordinary genetic algorithms (GAs) introduced in 2000s, for solving numerous optimization problems up to 2015. The aim is to provide a comprehensive picture of the development of the algorithms and the areas of implementation in literature yet. From the survey, a number of findings include: (1) number of studies tends to increase over the last five years dealing with various combinatorial optimization problems, however limited research deals with continuous variables, (2) local search procedure is the typical hybridization method used to enhance the performance of the algorithms, hence others improvement methods are still potential avenues for future research.