  Genetic Algorithm is an evolutionary algorithm and a metaheuristic that was
introduced to overcome the failure of gradient based method in solving the
optimization and search problems. The purpose of this paper is to evaluate the
impact on the convergence of Genetic Algorithm vis-a-vis 0/1 knapsack. By
keeping the number of generations and the initial population fixed, different
crossover methods like one point crossover and two-point crossover were
evaluated and juxtaposed with each other. In addition to this, the impact of
different selection methods like rank-selection, roulette wheel and tournament
selection were evaluated and compared. Our results indicate that convergence
rate of combination of one point crossover with tournament selection, with
respect to 0/1 knapsack problem that we considered, is the highest and thereby
most efficient in solving 0/1 knapsack.
