An Investigation into the Effectiveness of Different Methodologies in Enhancing Genetic Algorithm Performance

this examines investigates the impact of different algorithmic methodologies on improving the overall performance of genetic algorithms. A complete evaluate of the literature became carried out including both theoretical and empirical investigations. A complete of fourteen studies has been covered, with ten being theoretical and four being empirical in nature. The methodologies studied on this synthesis covered the use of operators inclusive of mutation, crossover, and choice, as well as strategies consisting of elitism, micro-mutation, and mimetic algorithms. Throughout all fourteen studies, the outcomes confirmed that the use of those methodologies can have a fine impact on enhancing the performance of a given genetic set of rules. However, the value of the development turned into found to vary considerably between the one of a kind methodologies and genetic set of rules structures. Therefore, it seems that a particular methodology ought to be cautiously chosen primarily based at the characteristics of the given problem domain. Additionally, similarly research should cognizance on exploring approaches to optimize the combination of various methodologies for stepped forward performance.