Different Approaches to Solve the 0 / 1 Knapsack Problem

The purpose of this paper is to analyze several algorithm design paradigms applied to a single problem – the 0/1 Knapsack Problem. The Knapsack problem is a combinatorial optimization problem where one has to maximize the benefit of objects in a knapsack without exceeding its capacity. It is an NP-complete problem and as such an exact solution for a large input is practically impossible to obtain. The main goal of the paper is to present a comparative study of the brute force, dynamic programming, memory functions, branch and bound, greedy, and genetic algorithms. The paper discusses the complexity of each algorithm in terms of time and memory requirements, and in terms of required programming efforts. Our experimental results show that the most promising approaches are dynamic programming and genetic algorithms. The paper examines in more details the specifics and the limitations of these two paradigms.