Skip to main content
Log in

Polyominoes tiling by a genetic algorithm

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

Abstract

Most existing placement algorithms were designed to handle blocks that are rectangular in shape. In this paper, we show how a genetic algorithm (GA) is used to construct an optimal arrangement of two-dimensional rectilinear blocks. Our approach does not require the orientation of each block to be fixed. To transform the placement problem to a GA problem, we devised a decoding technique known as circular placement. The novelty of the circular placement technique is that it configures the rectilinear blocks by building up potentially good groupings of blocks starting from the corners of the placement area. To complement the circular placement approach, we present a methodology for deriving a suitable objective function. We confirm the performance of our GA-based placement algorithm by presenting simulation results of some problems on tiling with up to 128 polyominoes. The algorithm described in this paper has great potential for applications in packing, compacting and general component placement in the various disciplines of engineering.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Y. Akiyama, A. Yamashita, M. Kajiura, and H. Aiso, “Combinatorial optimization with Gaussian machines,” Proceedings of International Joint Conference on Neural Networks'89, Washington, 1989, vol. 1, pp. 533–540.

    Google Scholar 

  2. Y. Davidor, Genetic Algorithms and robotics: A Heuristic Strategy for Optimization, World Scientific: Singapore, pp. 45–92, 1990.

  3. L. Davis, “Job shop scheduling with genetic algorithms,” Proceedings of First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, 1985, pp. 136–140.

  4. A.E. Dunlop and B.W. Kernighan, “A procedure for placement of standard-cell VLSI circuits,” IEEE Transactions on Computer Aided Design, vol. 4, no. 1, pp. 92–98, 1985.

    Google Scholar 

  5. M. Gardner, “Mathematical games,” Scientific American, pp. 112–115, 1975.

  6. D.E. Goldberg, Computer-Aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning, Doctoral Dissertation, University of Michigan, 1983.

  7. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, pp. 1–214, 1989,.

  8. D.E. Goldberg and R. Lingle, Jr., “Alleles, loci, and the traveling salesman problem,” Proceedings of First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, 1985, pp. 154–158.

  9. S.W. Golomb, Polyominoes, Scribner: New York, 1965.

    Google Scholar 

  10. S.W. Golomb, “Polyominoes which tile rectangles,” Journal of Combinatorial Theory, vol. A 51, pp. 117–124, 1989.

    Google Scholar 

  11. J.J. Grefenstette and J.E. Baker, “How genetic algorithms work: A critical look at implicit parallelism,” Proceedings of Third International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1989, pp. 20–27.

  12. J.J. Grefenstette and J.M. Fitzpatrick, “Genetic search with approximate function evaluations,” Proceedings of First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, 1985, pp. 112–120.

  13. J.H. Holland, Adaptation in Natural and Artificial Systems, University Michigan Press: Ann Arbor, MI, pp. 75–120, 1975.

    Google Scholar 

  14. J. Horn, D.E. Goldberg, and K. Deb, “Long path problems,” Proceedings of Third Conference of Parallel Problem Solving from Nature, Springer-Verlag, Berlin, Germany, 1994, pp. 149–158.

    Google Scholar 

  15. H. Kargupta, K. Deb, and D.E. Goldberg, “Ordering genetic algorithms and deception,” Proceedings of Second Conference on Parallel Problem Solving from Nature, Amsterdam, North-Holland, 1992, pp. 47–56.

  16. C.L. Karr, “Design of an adaptive fuzzy logic controller using a genetic algorithm,” Proceedings of Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1991, pp. 450–457.

  17. M.S. Klamkin and A. Liu, “Polyominoes on the infinite checkerboard,” Journal of Combinatorial Theory, vol. A 28, pp. 7–16, 1980.

    Google Scholar 

  18. D.A. Klarner, “Packing a rectangle with congruent N-ominoes,” Journal of Combinatorial Theory, vol. 7, pp. 107–115, 1969.

    Google Scholar 

  19. M.H. Lim, S. Rahardja, and B.H. Gwee, “A GA paradigm for learning fuzzy rules,” (to appear) in International Journal for Fuzzy Sets and Systems, North-Holland, 1995.

  20. H. Muhlenbein, “How genetic algorithms really work,” Proceedings of Second Conference on Parallel Problem Solving from Nature, Amsterdam, North-Holland, 1992, pp. 15–26.

  21. J.D. Schaffer, R.A. Caruana, L.J. Eshelman, and R. Das, “A study of control parameters affecting on-line performance of genetic algorithms for function optimization,” Proceedings of Third International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1989, pp. 51–60.

  22. S. Sutanthavibul, E. Shragowitz and J. B. Rosen, “An analytical approach to floorplan design and optimization,” IEEE Transactions on Computer Aided Design, vol. 10, no. 6, pp. 761–769, 1991.

    Google Scholar 

  23. Y. Takefuji and K.C. Lee, “A parallel algorithm for tiling problems,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 143–145, 1990.

    Google Scholar 

  24. T.C. Wang and D.F. Wong, “Optimal floorplan area optimization,” IEEE Transactions on Computer Aided Design, vol. 11, no. 10, pp. 992–1002, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gwee, B.H., Lim, M.H. Polyominoes tiling by a genetic algorithm. Comput Optim Applic 6, 273–291 (1996). https://doi.org/10.1007/BF00247795

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00247795

Keywords

Navigation