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Comparison of Deterministic and Probabilistic Approaches for Solving 0/1 Knapsack Problem

  • Ritika Mahajan
  • Sarvesh Chopra
  • Sonika Jindal
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

The purpose of this paper is to analyze 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 and Genetic Algorithm affect the performance of Knapsack Problem. Our experimental results show that the promising approach is genetic algorithm as it gives result in optimal time.

Keywords

Knapsack Problem NP-complete problem Dynamic Programming Genetic Algorithm 

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References

  1. Cormen, T.H., Leiserson, C.E., Riverst, R.L., Stein, C.: Introduction to AlgorithmsGoogle Scholar
  2. Poirriez, V., Yanev, N., Andonov, R.: A Hybrid Algorithm for the Unbounded Knapsack Problem (October 28, 2008)Google Scholar
  3. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley (1990)Google Scholar
  4. Mitchell, M.: An Introduction to Genetic Algorithms. MIT PressGoogle Scholar
  5. Thesis by Shailendra Kumar, Choosing Best Algorithm Design Strategies For a Particular Problem (June 2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Computer Science EngineeringShaheed Bhagat Singh College of Engineering and TechnologyFerozepurIndia
  2. 2.Information TechnologyGuru Nanak Dev Engineering CollegeLudhianaIndia
  3. 3.Department of Computer Science and EngineeringShaheed Bhagat Singh College of Engineering and TechnologyFerozepurIndia

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