Research Output

Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics.

  The idea underlying hyper-heuristics is to discover some
combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messy-GA-based approach that learns such a heuris-
tic combination for solving one-dimensional bin-packing problems. When applied to a large set of benchmark problems, the learned procedure finds an optimal solution for nearly 80% of them, and for the rest produces an
answer very close to optimal. When compared with its own constituent heuristics, it ranks first in 98% of the problems.

  • Date:

    12 July 2003

  • Publication Status:

    Published

Citation

Ross, P., Marin-Blazquez, J. G., Schulenburg, S. & Hart, E. (2003). Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics.

Authors

Keywords

Computer programming; Problem solving; Hyper-heuristics; Genetic algorithms; Bin-packing; Unidimensional;

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