On the similarities between binary-coded GA and real-coded GA in wide search space

Abstract

This study sets out to identify a real-world system by employing binary-coded and real-coded genetic algorithms (GAs). However, in a nascent stage of setting configurations of GAs, it is difficult to determine the feasible boundaries of each parameter of the system. In this paper, both GAs are implemented based on the similar mechanisms of crossover and mutation, and performed on discretized linear, logarithmic, and hybrid search spaces with corresponding encoding methods. Through simple probabilistic analysis, it follows that logarithmic space and hybrid space searches are far more advantageous to general linear space search in wide bound searching. The identification results of a simple electrical circuit support this expectation and confirm that real-coded GA (RCGA) of logarithmic and hybrid space searches are the best way to tackle the real-world problem.