hGRGA: A scalable genetic algorithm using homologous gene schema replacement

Abstract In this article, we propose a new evolutionary algorithm, referred as h omologous G ene R eplacement G enetic A lgorithm (hGRGA) that includes a novel and generic operator called h omologous G ene R eplacement (hGR). The hGR operator improves the chromosomes in gene level to promote their overall functionality. The hGRGA effectively encodes the key idea of the natural evolutionary process that locates and utilizes good local schema present in the genes of a chromosome through hGR operator. The proposed hGRGA is evaluated and compared with two variants of GA and two other state-of-the-art evolutionary computing algorithms based on widely-used benchmark functions with a motivation to apply to wider varieties of optimization problems. The simulation results show that the new algorithm can offer faster convergence and better precision while finding optima. Our analysis shows that hGR is effectively a scalable operator that makes hGRGA well suited for real world problems with increasing size and complexity.