Critical Analysis of Genetic Algorithm under Crossover and Mutation Rate

Many evolutionary algorithms have been developed by the researcher. Genetic algorithms and memetic algorithms are most commonly used by researchers to solve various optimization problems. Genetic and Memetic algorithms have certain parameters that need to be modified so that the algorithm can produce better results. In the present paper, we are working on a genetic algorithm. Crossover operators and mutation operators play a very important role in the development of an efficient genetic algorithm (GA). The performance of the genetic algorithm depends upon its parameter setting. In the present paper, the basic conceptual features and unique characteristics of the different rates of crossover and mutation operators are discussed. The simulation environment was created to study the combinatorial problems, i.e., scheduling and sequencing problems. Currently, the researcher is focusing on the scheduling problem which is a NP-Hard problem. In the future study, the traveling salesman problem could be considered to investigate the additional parameters of GA. The current empirical study shows that the crossover and mutation rate have an impact on the GA's performance. This research helps us to understand how to choose the right crossover and mutation rate so that the GA's performance can be improved.