Research Output

Hyper-heuristics: learning to combine simple heuristics in bin-packing problems.

  Evolutionary algorithms (EAs) often appear to be a ‘black box’, neither offering worst-case bounds nor any guarantee of optimality when used to solve individual problems. They can also take much longer than non-evolutionary methods. We
try to address these concerns by using an EA, in
particular the learning classifier system XCS, to
learn a solution process rather than to solve individual
problems. The process chooses one of various simple non-evolutionary heuristics to apply to each state of a problem, gradually transforming the problem from its initial state to a solved state. We test this on a large set of one dimensional bin packing problems. For some of
the problems, none of the heuristics used can find
an optimal answer; however, the evolved solution
process can find an optimal solution in over 78% of cases.

  • Date:

    09 July 2002

  • Publication Status:

    Published

Citation

Ross, P., Schulenburg, S., Marin-Blazquez, J. G. & Hart, E. (2002). Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. ISBN 1558608788

Authors

Keywords

Evolutionary algorithms; Limitations; Learning solutions; Empirical process; Systematic packing;

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