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Nature inspired genetic algorithms for hard packing problems

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  • Philipp Rohlfshagen
  • John Bullinaria

Abstract

This paper presents two novel genetic algorithms (GAs) for hard industrially relevant packing problems. The design of both algorithms is inspired by aspects of molecular genetics, in particular, the modular exon-intron structure of eukaryotic genes. Two representative packing problems are used to test the utility of the proposed approach: the bin packing problem (BPP) and the multiple knapsack problem (MKP). The algorithm for the BPP, the exon shuffling GA (ESGA), is a steady-state GA with a sophisticated crossover operator that makes maximum use of the principle of natural selection to evolve feasible solutions with no explicit verification of constraint violations. The second algorithm, the Exonic GA (ExGA), implements an RNA inspired adaptive repair function necessary for the highly constrained MKP. Three different variants of this algorithm are presented and compared, which evolve a partial ordering of items using a segmented encoding that is utilised in the repair of infeasible solutions. All algorithms are tested on a range of benchmark problems, and the results indicate a very high degree of accuracy and reliability compared to other approaches in the literature. Copyright Springer Science+Business Media, LLC 2010

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  • Philipp Rohlfshagen & John Bullinaria, 2010. "Nature inspired genetic algorithms for hard packing problems," Annals of Operations Research, Springer, vol. 179(1), pages 393-419, September.
    • Handle: RePEc:spr:annopr:v:179:y:2010:i:1:p:393-419:10.1007/s10479-008-0464-5
    DOI: 10.1007/s10479-008-0464-5
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    1. Scholl, Armin & Klein, Robert & Jürgens, Christian, 1996. "BISON : a fast hybrid procedure for exactly solving the one-dimensional bin packing problem," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 49135, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    2. Delorme, Maxence & Iori, Manuel & Martello, Silvano, 2016. "Bin packing and cutting stock problems: Mathematical models and exact algorithms," European Journal of Operational Research, Elsevier, vol. 255(1), pages 1-20.

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