Comparative Performance Analysis of Genetic Algorithm Variants on Solving 0/1 Knapsack Problem

Practical optimization issues are primarily and commonly addressed by EAs(Evolutionary Algorithms). A wellliked subset of EA, the Genetic Algorithm (GA), is renowned for its capacity to solve various optimization problems. This research work examines the possibility of the Genetic Algorithm (GA) to resolve the 0/1 Knapsack problem. In this study, a number of mutation and crossover types are used to solve 0/1 Knapsack problem. The effectiveness of GA is compared by utilizing various crossover and mutation combinations. Positively, GA may solve the problems substantially faster and with a reasonable degree of accuracy as compared to traditional methods. In a careful assessment of the stated behavior of GA, combination on three mutations and three crossover methods are explored in this study.