Binary Differential Evolution with a Limited Maximum Number of Dimension Changes

Evolutionary Algorithms (EAs) are those based on the phenomenon of survival of the fittest. Differential Evolution (DE) is a member of this family, and it is well-suited for handling problems with real-valued variables. However, to use DE to solve binary problems, it is necessary to employ some adaptation. The primary objective of the present study is to develop a new binary version of DE. The proposed algorithm is called Binary Differential Evolution with a limited maximum number of dimension changes (NBDE), and it was tested with the OneMax Problem, five variants of the Knapsack Problem (KP), and Feature Selection (FS). The results showed that NBDE is competitive and superior to the other tested algorithms in many instances. For the 0/1 KP and 0/1 Multidimensional KP, NBDE outperforms all the other algorithms for all instances. For the FS problem, the proposed algorithm demonstrates good accuracy as its primary quality. The proposed algorithm exhibits a satisfactory performance, particularly in high-dimensional problems, which appears to be a quality inherited from the method that inspired its creation. This is particularly interesting because it provides empirical evidence that the importation of operators can perpetuate a pattern of behavior in algorithms with different structures.