Heuristic generation via parameter tuning for online bin packing

Yarimcam, Ahmet and Asta, Shahriar and Özcan, Ender and Parkes, Andrew J. (2014) Heuristic generation via parameter tuning for online bin packing. In: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS), 9-12 Dec 2014, Orlando, Florida.

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Abstract

Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/741019
Additional Information: Published in: EALS 2014: 2014 IEEE International Symposium on Evolving and Autonomous Learning Systems : proceedings. Piscataway, N.J. : IEEE, c2014, p. 102-108. ISBN: 9781479944958, doi: 10.1109/EALS.2014.7009510
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1109/EALS.2014.7009510
Depositing User: Ozcan, Dr Ender
Date Deposited: 27 Jun 2016 09:28
Last Modified: 04 May 2020 16:58
URI: http://eprints.nottingham.ac.uk/id/eprint/34400

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