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The Role of Representations in Dynamic Knapsack Problems

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

The effect of different representations has been thoroughly analyzed for evolutionary algorithms in stationary environments. However, the role of representations in dynamic environments has been largely neglected so far. In this paper, we empirically compare and analyze three different representations on the basis of a dynamic multi-dimensional knapsack problem. Our results indicate that indirect representations are particularly suitable for the dynamic multi-dimensional knapsack problem, because they implicitly provide a heuristic adaptation mechanism that improves the current solutions after a change.

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References

  1. Beasley, J.E.: Or-library. online, http://www.brunel.ac.uk/depts/ma/research/jeb/orlib/mknapinfo.html

  2. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)

    Google Scholar 

  3. Branke, J., Salihoglu, E., Uyar, S.: Towards an analysis of dynamic environments. In: Genetic and Evolutionary Computation Conference, pp. 1433–1439. ACM, New York (2005)

    Google Scholar 

  4. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)

    Google Scholar 

  5. GLPK. GNU linear programming kit. online, http://www.gnu.org/software/glpk/glpk.html

  6. Gottlieb, J.: Evolutionary Algorithms for Combinatorial Optimization Problems. Phd, Technical University Clausthal, Germany (December 1999)

    Google Scholar 

  7. Gottlieb, J.: Permutation-based evolutionary algorithms for multidimensional knapsack problems. In: ACM Symposium on Applied Computing, vol. 1, pp. 408–414. ACM, New York (2000)

    Chapter  Google Scholar 

  8. Gottlieb, J.: On the feasibility problem of penalty-based evolutionary algorithms for knapsack problems. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 50–60. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Raidl, G.R., Gottlieb, J.: The effects of locality on the dynamics of decoder-based evolutionary search. In: Genetic and Evolutionary Computation Conference, pp. 283–290. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  10. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)

    Article  Google Scholar 

  11. Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  12. Morrison, R.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  13. Pirkul, H.: A heuristic solution procedure for the multiconstraint zero-one knapsack problem. Naval Research Logistics 34, 161–172 (1987)

    Article  MATH  Google Scholar 

  14. Raidl, G.R.: Weight-codings in a genetic algorithm for the multiconstraint knapsack problem. In: Congress on Evolutionary Computation, pp. 596–603. IEEE, Los Alamitos (1999)

    Google Scholar 

  15. Raidl, G.R., Gottlieb, J.: Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: A case study for the multidimensional knapsack problem. Evolutionary Computation 13(4) (2005)

    Google Scholar 

  16. Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms. Physica (2002)

    Google Scholar 

  17. Weicker, K.: Evolutionary Algorithms and Dynamic Optimization Problems. Der Andere Verlag (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute AIFB, University of Karlsruhe, Karlsruhe, Germany

    Jürgen Branke

  2. Informatics Institute, Istanbul Technical University, Istanbul, Turkey

    Merve Orbayı

  3. Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey

    Şima Uyar

Authors
  1. Jürgen Branke
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  2. Merve Orbayı
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  3. Şima Uyar
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

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© 2006 Springer-Verlag Berlin Heidelberg

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Branke, J., Orbayı, M., Uyar, Ş. (2006). The Role of Representations in Dynamic Knapsack Problems. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_74

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  • DOI: https://doi.org/10.1007/11732242_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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