Abstract
In this paper the utility of several constraint-handling techniques is investigated on the basis of a family of 0/1 knapsack problems. Several evolutionary algorithms are applied to this NP-hard problem. The conclusions might be applicable to many constrained combinatorial optimization problems, for which the use of evolutionary algorithm is considered.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Arabas, J., Michalewicz, Z., and Mulawka, J., GAVaPS — a Genetic algorithm with Varying Population Size, Technical Report 001-1994, Department of Computer Science, University of North Carolina, Charlotte, 1994.
Bäck, T., Hoffmeister, F., and Schwefel, H.-P., A Survey of Evolution Strategies, in [3], pp.2–9
Belew, R. and Booker, L. (Editors), Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 1991.
Davis, L., (Editor), Genetic Algorithms and Simulated Annealing, Morgan Kaufmann Publishers, Inc., Los Altos, CA, 1987.
Forrest, S. (Editor), Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 1993.
Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
Khuri, S., Bäck, T., and Heitkötter, J., The Zero/One Multiple Knapsack Problem and Genetic Algorithms, Proceedings of the ACM Symposium of Applied Computation (SAC '94).
Martello, S. and Toth, P., Knapsack Problems, John Wiley & Sons, Chichester, UK, 1990.
Michalewicz, Z., Genetic Algorithms + Data Structures = Evolution Programs, Springer Verlag, 1992.
Michalewicz, Z., A Hierarchy of Evolution Programs: An Experimental Study, Evolutionary Computation, Vol.1, No.1, 1993, pp.51–76.
Michalewicz, Z. (Ed.), Statistics & Computing, special issue on evolutionary computation, 1994.
Michalewicz, Z., and Attia, N., Evolutionary Optimization of Constrained Problems, 3rd Annual Conference on Evolutionary Programming, February 24–26, 1994, San Diego.
Orvosh, D. and Davis, L., Shall We Repair? Genetic Algorithms, Combinatorial Optimization, and Feasibility Constraints, in [5], p.650.
Richardson, J.T., Palmer, M.R., Liepins, G., and Hilliard, M., Some Guidelines for Genetic Algorithms with Penalty Functions, in [15], pp.191–197.
Schaffer, J., (Editor), Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, CA, 1989.
Siedlecki, W. and Sklanski, J., Constrained Genetic Optimization via Dynamic Reward-Penalty Balancing and Its Use in Pattern Recognition, in [15], pp. 141–150.
Smith, A. and Tate, D., Genetic Optimization Using A Penalty Function, in [5], pp.499–503.
Starkweather, T., McDaniel, S., Mathias, K., Whitley, C., and Whitley, D., A Comparison of Genetic Sequencing Operators, in [3], pp.69–76.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Michalewicz, Z., Arabas, J. (1994). Genetic algorithms for the 0/1 knapsack problem. In: Raś, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1994. Lecture Notes in Computer Science, vol 869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58495-1_14
Download citation
DOI: https://doi.org/10.1007/3-540-58495-1_14
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58495-7
Online ISBN: 978-3-540-49010-4
eBook Packages: Springer Book Archive