Abstract
In this paper we investigate the use of two evolutionary based heuristic to the bin packing problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. Unlike other evolutionary based heuristics used with optimization problems, ours do not use domain-specific knowledge and has no specialized genetic operators. It uses a straightforward fitness function to which a graded penalty term is added to penalize infeasible strings. The encoding of the problem makes use of strings that are of integer value. Strings do not represent permutations of the objects as is the case in most approaches to this problem. We use a different representation and give justifications for our choice. Several problem instances are used with a greedy heuristic and the evolutionary based algorithms. We compare the results and conclude with some observations, and suggestions on the use of evolutionary heuristics for combinatorial optimization problems.
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References
Th. Bäck and M. Schütz. Evolution strategies for mixed- integer optimization of optical multilayer systems. In Proceedings of the 4th Annual Conference on Evolutionary Programming, 1995. (accepted for publication).
J. E. Beasley. OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society, 41 (11): 1069–1072, 1990.
A. L. Corcoran and R. L. Wainwright. Libga: A user-friendly workbench for order-based genetic algorithm research. In Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing, pages 111–117. ACM, ACM Press, February 1993.
E. Falkenaur. A new representation and operators for genetic algorithms applied to grouping problems. Evolutionary Computation, 2 (2): 123–144, 1994.
M. R. Garey and D. S. Johnson. Computers and Intractability — A Guide to the Theory of NP-Completeness. Freemann Sz Co., San Francisco, CA, 1979.
D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, MA, 1989.
J. H. Holland. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI, 1975.
S. Khuri, T. Bäck, and J. Heitkötter. An evolutionary approach to combinatorial optimization problems. In D. Cizmar, editor, Proceedings of the 22 nd Annual ACM Computer Science Conference, pages 66–73. ACM, ACM Press, 1994.
S. Martello and P. Toth. Knapsack Problems: Algorithms and Computer Implementations. Wiley, Chichester, West Sussex, England, 1990.
C. H. Papadimitriou. Computational Complexity. Addison Wesley, Reading, MA, 1994.
M. Schütz. Eine Evolutionsstrategie für gemischt-ganzzahlige Optimierungsprobleme mit variabler Dimension. Diplomarbeit, Universität Dortmund, Fachbereich Informatik, 1994.
H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995.
D. R. Stinson. An Introduction to the Design and Analysis of Algorithms. The Charles Babbage Research Center, Winnipeg, Manitoba, Canada, 2nd edition, 1987.
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Khuri, S., Schütz, M., Heitkötter, J. (1995). Evolutionary Heuristics for the Bin Packing Problem. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_75
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_75
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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