State‐Of‐The‐Art on Ensemble of Real Coded Genetic Algorithm Operators

ABSTRACT Real coded genetic algorithms (RCGAs) are among the most versatile metaheuristic algorithms used to determine the global optimal solutions for nonlinear optimization problems, aimed at solving real‐life complex optimization problems. After the selection operator, the crossover and mutation operators are two crucial strategies upon which the performance of a genetic algorithm (GA) depends. At present, various types of crossover and mutation operators are available in the literature. Therefore, this study attempts to present a state‐of‐the‐art review on real coded crossover operators and real coded mutation operators. Each operator is explained with the help of examples. The objective of this state‐of‐the‐art paper is to serve as a foundation for researchers who wish to design new real coded crossover operators or mutation operators. Moreover, to evaluate the effectiveness of various variants of RCGA, 23 classical benchmark problems are utilised, offering insights into their performance across different optimization problems.