A faster genetic algorithm to solve Knapsack problem employing fuzzy technique

Knapsack problem is an optimization problem which is classified as NP-hard problem. Genetic algorithms (GAs) are being used extensively in optimization problems as an alternative to traditional heuristics. The study aims at proposing some of the technique to find faster convergence and better quality solution with the help of Genetic algorithm along with fuzzy. Firstly, every item is given a linguistic term so that each and every item can be monitored separately. Then each chromosome is classified as a whole. A new selection technique is proposed to maintain the diversity of the population. Crossover is done based on classification of chromosome. Justification of when to do single point and when to do multipoint crossover is given. A new Repair operator is proposed to make the infeasible solution feasible. The proposed algorithm does not stuck in the local maxima rather it explores the whole search space and new selection and crossover technique is used to ensure that. The proposed technique is compared with some other algorithm for solving Knapsack problem published in literature The result indicate that the proposed algorithm is effective in finding solution of better quality and provide faster convergence.