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The 0/1 Knapsack Problem is an optimization problem solved using various soft computing methods. The solution to the 0/1 Knapsack Problem (KP) can be viewed as the result of a sequence of decisions. Simple Genetic Algorithm (SGA) effectively solves knapsack problem for large data set. But it has problems like premature convergence and population diversity. Dual Population Genetic Algorithm (DPGA) is an improved version of Genetic Algorithm (GA) with the solution to above problems. This paper proposes Dual Population GA for solving 0/1 knapsack Problem. Experimental results of knapsack on SGA and DPGA are compared on standard as well as random data sets. The experimental result shows DPGA performs better than knapsack on SGA.

0/1 Knapsack Problem using Diversity based Dual Population Genetic Algorithm