2012Unpublished venueRequires access

Analysis of 0/1 Knapsack Problem Using Deterministic and Probabilistic Techniques

Ritika Mahajan, Sarvesh Chopra

Open publisher page 5 citations

Abstract

The purpose of this paper is to analyze several algorithm design paradigms applied to single problem - 0/1 Knapsack Problem. The Knapsack Problem is a combinatorial optimization problem where one has to maximize the benefits of objects in a knapsack without exceeding its capacity. It is an NP-complete problem and uses exact and heuristic techniques to get solved. The objective is to analyze that how the various techniques like Dynamic Programming, Greedy Algorithm and Genetic Algorithm affect the performance of Knapsack Problem.

About this research paper

What this paper is about

The purpose of this paper is to analyze several algorithm design paradigms applied to single problem - 0/1 Knapsack Problem. The Knapsack Problem is a combinatorial optimization problem where one has to maximize the benefits of objects in a knapsack without exceeding its capacity. It is an NP-complete problem and uses exact and heuristic techniques to get solved. The objective is to analyze that how the various techniques like Dynamic Programming, Greedy Algorithm and Genetic Algorithm affect the performance of Knapsack Problem.

Why it matters

OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

The purpose of this paper is to analyze several algorithm design paradigms applied to single problem - 0/1 Knapsack Problem. The Knapsack Problem is a combinatorial optimization problem where one has to maximize the benefits of objects in a knapsack without exceeding its capacity. It is an NP-complete problem and uses exact and heuristic techniques to get solved. The objective is to analyze that how the various techniques like Dynamic Programming, Greedy Algorithm and Genetic Algorithm affect the performance of Knapsack Problem.

Key concepts: Knapsack problem, Continuous knapsack problem, Change-making problem, Mathematical optimization, Cutting stock problem, Polynomial-time approximation scheme, Generalized assignment problem, Greedy algorithm

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