Sami Khuri, Martin Schutz
In this paper we investigate the use of two evolutionary based heuristics for the bin packing problem (BPP). The intractability of this problem serves as a motivation for developing heuristics that yield approximate solutions. Unlike other evolutionary heuristics employed in optimization problems, our approach does not utilize domain-specific knowledge and lacks specialized genetic operators. Instead, it makes use of a straightforward fitness function enhanced by a graded penalty term to penalize infeasible solutions. The encoding in our method involves strings of integer values, as opposed to the permutation representations used in most existing approaches. We apply a different representation and provide justifications for this choice. Several problem instances are tested using both a greedy heuristic and the proposed evolutionary algorithms, and we perform a comparative analysis of the results. The findings lead us to several observations and suggestions on the effective application of evolutionary heuristics for combinatorial optimization problems.
@article{6a5a13c3-fbf2-4bda-98af-c06693cccdd4,
title={Evolutionary Heuristics for the Bin Pack},
author={Sami Khuri and Martin Schutz},
year={2026},
language={en}
}TY - JOUR TI - Evolutionary Heuristics for the Bin Pack AU - Sami Khuri AU - Martin Schutz PY - 2026 LA - en ER -
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