Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Science and Its Applications

ICCSA 2006: Computational Science and Its Applications - ICCSA 2006 pp 678–687Cite as

Optimization of Performance of Genetic Algorithm for 0-1 Knapsack Problems Using Taguchi Method

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3982))

Abstract

In this paper, a genetic algorithm (GA) is developed for solving 0-1 knapsack problems (KPs) and performance of the GA is optimized using Taguchi method (TM). In addition to population size, crossover rate, and mutation rate, three types of crossover operators and three types of reproduction operators are taken into account for solving different 0-1 KPs, each has differently configured in terms of size of the problem and the correlation among weights and profits of items. Three sizes and three types of instances are generated for 0-1 KPs and optimal values of the genetic operators for different types of instances are investigated by using TM. We discussed not only how to determine the significantly effective parameters for GA developed for 0-1 KPs using TM, but also trace how the optimum values of the parameters vary regarding to the structure of the problem.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Martello, S., Toth, P.: Knapsack Problems Algorithms and Computer Implementations. John Wiley&Sons, England (1990)

    MATH  Google Scholar 

  2. Sakawa, M., Kato, K.: Genetic Algorithms with Double Strings for 0-1 Programming Problems. European Journal of Operational Research 144, 581–597 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  3. Bortfeldt, A., Gehring, H.: A Hybrid Genetic Algorithm for the Container Loading Problem. European Journal of Operational Research 131, 143–161 (2001)

    Article  MATH  Google Scholar 

  4. Taguchi, T., Yokota, T.: Optimal Design Problem of System Reliability with Interval Co-efficient Using Improved Genetic Algorithms. Computers and Industrial Engineering 37, 145–149 (1999)

    Article  Google Scholar 

  5. Michelcic, S., Slivnik, T., Vilfan, B.: The Optimal Cut of Sheet Metal Belts into Pieces of Given Dimensions. Engineering Structures 19, 1043–1049 (1997)

    Article  Google Scholar 

  6. Iyer, S.K., Saxena, B.: Improved Genetic Algorithm for the Permutation Flowshop Scheduling Problem. Computers and Operations Research 31, 593–606 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  7. Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley&Sons, New York (1997)

    Google Scholar 

  8. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  9. Taguchi, G.: Systems of Experimental Design. Unipub Kraus International Publishers, New York (1987)

    Google Scholar 

  10. Georgilakis, P., Hatziargyriou, N., Paparigas, D., Elefsiniotis, S.: Effective Use of Magnetic Materials in Transformer Manufacturing. Journal of Materials Processing Technology 108, 209–212 (2001)

    Article  Google Scholar 

  11. Antony, J., Roy, R.K.: Improving the Process Quality Using Statistical Design of Experiments: A Case Study. Quality Assurance 6, 87–95 (1999)

    Google Scholar 

  12. Fowlkes, W.Y., Creveling, C.M.: Engineering Methods for Robust Product Design. Addison-Wesley, Reading (1995)

    Google Scholar 

  13. Ross, P.J.: Taguchi Techniques for Quality Engineering, 2nd edn. McGraw-Hill, New York (1996)

    Google Scholar 

  14. Nian, C.Y., Yang, W.H., Tarng, Y.S.: Optimization of Turning Operations with Multiple Performance Characteristics. Journal of Materials Processing Technology 95, 90–96 (1999)

    Article  Google Scholar 

  15. Martello, S., Pisinger, D., Toth, P.: New Trends in Exact Algorithms for the 0-1 Knapsack Problem. European Journal of Operational Research 123, 325–332 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  16. Wu, D.R., Tsai, Y.J., Yen, Y.T.: Robust Design of Quartz Crystal Microbalance Using Finite Element and Taguchi Method. Sensors and Actuators B92, 337–344 (2003)

    Article  Google Scholar 

  17. Roy, R.K.: A Primer on the Taguchi Method. VNR Publishers, New York (1999)

    Google Scholar 

  18. Ozalp, A., Anagun, A.S.: Analyzing Performance of Artificial Neural Networks by Taguchi Method: Forecasting Stock Market Prices. Journal of Statistical Research 2, 29–45 (2003)

    Google Scholar 

  19. Lin, T.R.: Experimental Design and Performance Analysis of TiN-coated Carbide Tool in Face Milling Stainless Steel. Journal of Materials Processing Technology 127, 1–7 (2002)

    Article  Google Scholar 

  20. Syrcos, G.P.: Die Casting Process Optimization Using Taguchi Methods. Journal of Materials Processing Technology 135, 68–74 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Industrial Engineering Department, Eskisehir Osmangazi University, Bademlik 26030, Eskisehir, Turkey

    A. S. Anagun & T. Sarac

Authors
  1. A. S. Anagun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Sarac
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina Gavrilova

  2. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  5. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  6. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  7. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  8. School of Information and Communication Engineering, Sungkyunkwan University, Korea

    Hyunseung Choo

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Anagun, A.S., Sarac, T. (2006). Optimization of Performance of Genetic Algorithm for 0-1 Knapsack Problems Using Taguchi Method. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751595_72

Download citation

  • DOI: https://doi.org/10.1007/11751595_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34075-1

  • Online ISBN: 978-3-540-34076-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation