Applying greedy genetic algorithm on 0/1 multiple knapsack problem

Knapsack problem is a well-known optimization problem in computer science. It has many application areas in science and engineering. Knapsack problem can be solved using genetic algorithm. Multiple knapsack problem (MKP) is a special form of knapsack problem in which items are to be placed in more than one knapsack. Many researchers solve MKP problem using different techniques such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). The objective of this paper is to solve MKP problem using GA in an efficient manner. In this paper MKP is solved using greedy genetic algorithm. The proposed genetic algorithm uses greedy approach in its selection and reproduction operations of GA. The proposed greedy genetic algorithm is implemented on a standard data set and results ensure that the proposed greedy algorithm performs better than the standard genetic algorithm.