Genetic Algorithms with Adaptive Meta-heuristics for 2D Packing Problem

  • Romuald Jagielski
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)


In this paper we have proposed meta-heuristics for 2D packing problem. These heuristics are based on quasi-ordering and are adaptive. The meta-heuristics combined together with genetic algorithms supplied with appropriate parameters produce considerably improved, optimal or near-optimal, results.


Genetic Algorithm Packing Problem Standard Genetic Algorithm Optimum Height Simple Partitioning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Hopper, E. B.C.H. Turton An empirical investigation of meta-heuristic and heuristic algorithms for 2D packing problem, European Journal of Operational Research 128 (2001) 34–57MATHCrossRefGoogle Scholar
  2. [2]
    Jakobs, S. On genetic algorithms for the packing of polygons, European Journal of Operational Research 88 (1996) 165–181Google Scholar
  3. [3]
    Khuri, S. Evolutionary Heuristics for the Bin Packing Problem, Artificial Neural Nets and Genetic Algorithms, Proceeding of the International Conference, Ales, France, 1995Google Scholar
  4. [4]
    Liu, D., Hongfei Teng An improved BL-algorithm for genetic algorithm of the orthogonal packing of rectangles, European Journal of Operational Research 112 (1999) 413–420CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Romuald Jagielski
    • 1
  1. 1.School of Information TechnologySwinburne University of TechnologyMelbourneAustralia

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