Abstract
We present an assessment of the performance of a new on-line bin packing algorithm, which can interpolate smoothly from the Next Fit to Best Fit algorithms, as well as encompassing a new class of heuristic which packs multiple blocks at once. The performance of this novel O(n) on-line algorithm can be better than that of the Best Fit algorithm. The new algorithm runs about an order of magnitude slower than Next Fit, and about two orders of magnitude faster than Best Fit, on large sample problems. It can be tuned for optimality in performance by adjusting parameters which set its working memory usage, and exhibits a sharp threshold in this optimal parameter space as time constraint is varied. These optimality concerns provide a testbed for the investigation of the value of memory and attention-like properties to algorithms.
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Izumi, T., Yokomaru, T., Takahashi, A., Kajitani, Y.: Computational complexity analysis of Set-Bin-Packing problem. IEICE Transactions on Fundamentals Of Electronics Communications and Computer Sciences: 5 (1998) 842–849
Garey, M. R., Johnson, D. S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, CA. (1979)
Karp, R. M.: Reducibility Among Combinatorial Problems. In Complexity of Computer Computations, R. E. Miller and J. W. Thatcher eds. Plenum Press, NY. 1972 85–104
Johnson, D. S.: Fast Algorithms for Bin-Packing. Journal of Computer Systems Science 8 (1974) 272–314
Johnson, D. S., Demers, A., Ullman, J. D., Garey, M. R., Graham, R. L.: Worst-Case Performance Bounds for Simple One-Dimensional Packing Algorithms. SIAM Journal of Computing 3 (1974) 299–326
Mao, W.: Tight Worst-case Performance Bounds for Next-k-Fit Bin Packing. SIAM Journal on Computing 22(1) (1993) 46–56
Falkenauer, E.: A Hybrid Grouping Genetic Algorithm for Bin Packing. Working paper CRIF Industrial Management and Automation, CP 106-P4, 50 av. F. D.Roosevelt, B-1050 Brussels, Belgium. (1996)
Monasson, R., Zecchina, R., Kirkpatrick, S., Selman, B., Troyansky, L.: Determining computational complexity from characteristic ‘phase transitions’. Nature 400(6740) (1999) 133–137
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© 2001 Springer-Verlag Berlin Heidelberg
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Billock, J.G., Psaltis, D., Koch, C. (2001). The Match Fit Algorithm - A Testbed for Computational Motivation of Attention. In: Alexandrov, V.N., Dongarra, J.J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds) Computational Science - ICCS 2001. ICCS 2001. Lecture Notes in Computer Science, vol 2074. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45718-6_23
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DOI: https://doi.org/10.1007/3-540-45718-6_23
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