A Evolutionary Multiobjective Genetic Algorithm to Solve 0/1 Knapsack Problem Subhra Swetanisha 1 , Prof. Bhabani Sankar Prasad Mishra 2 1 Lect. Comp.Sc Dept , Trident Academy Of Technology, Bhubaneswar 2 KIIT University, Bhubaneswar + Corresponding author. E-mail address: swetanisha.subhra@gmail.com Abstract. The 0/1 Knapsack Problem is very well known and it appears in many real life world with different application. The solution to the multi objective 0/1 Knapsack problem can be viewed as the result of a sequence of decisions. The problem is NP - complete and it also generalization of the 0/1 Knapsack problem in which many Knapsack are considered. A evolutionary algorithm for solving multi objective 0/1 Knapsack Problem is introduced in this paper. This algorithm used a genetic algorithm for direct comparison of two solutions. Our motivation for developing a genetic algorithm in this paper in a multi objective optimization framework was that ( a) GA is robust search method. ( b) GA performs the global search. ( c ) GA already work with a population of candidate solutions, which makes them naturally suitable for multi objective problem solving, where this algorithm is used to consider a set of optimal solutions at each iteration. Few numerical experiments are realized using the best and recent algorithm in this paper. Experimental outcome show that the new proposed algorithm performs better. Keywords: 0/1 Knapsack Problem, Evolutionary multi objective optimization. Genetic algorithm, NP completeness. 1. Introduction Due to practical importance the 0/1 Knapsack Problem is widely used. In last few years the generalization of this problem has been studied and many algorithms have been proposed. Evolutionary approach for solving the multi-objective 0/1 Knapsack Problem is one of them, many real worked papers founded in the literature about multi-objective Knapsack Problem and about the algorithms introduced for solving them. ([6], [7], [8], [9]). In this paper we introduced a new evolutionary approach for multi objective 0/1 Knapsack Problem. We use the genetic algorithm in order to determine the solution quality. 2. Problem Description The knapsack problem [8] [9] is a problem in combinatorial optimization. Given a set of items, each with a cost and a value, determine the number of each item to include in a collection so that the total cost is least than a given limit and the total value is as large as possible. We have n kind of items. 1 through n, each item i has a value Pi and a weight Wi. The maximum weight that we can carry the knapsack is C. 221 2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) © (2011) IACSIT Press, Singapore