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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beasley, J.E.: Or-library. online, http://www.brunel.ac.uk/depts/ma/research/jeb/orlib/mknapinfo.html
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)
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)
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)
GLPK. GNU linear programming kit. online, http://www.gnu.org/software/glpk/glpk.html
Gottlieb, J.: Evolutionary Algorithms for Combinatorial Optimization Problems. Phd, Technical University Clausthal, Germany (December 1999)
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)
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)
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)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)
Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Heidelberg (2004)
Morrison, R.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Heidelberg (2004)
Pirkul, H.: A heuristic solution procedure for the multiconstraint zero-one knapsack problem. Naval Research Logistics 34, 161–172 (1987)
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)
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)
Rothlauf, F.: Representations for Genetic and Evolutionary Algorithms. Physica (2002)
Weicker, K.: Evolutionary Algorithms and Dynamic Optimization Problems. Der Andere Verlag (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)