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Efficient adaptive implementation of the serial schedule generation scheme using preprocessing and bloom filters

Karapetyan, D and Vernitski, A (2017) Efficient adaptive implementation of the serial schedule generation scheme using preprocessing and bloom filters. In: International Conference on Learning and Intelligent Optimization: LION 2017, 2017-06-19 - 2017-06-21, Nizhny Novgorod, Russia.

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Abstract

The majority of scheduling metaheuristics use indirect representation of solutions as a way to efficiently explore the search space. Thus, a crucial part of such metaheuristics is a “schedule generation scheme” – procedure translating the indirect solution representation into a schedule. Schedule generation scheme is used every time a new candidate solution needs to be evaluated. Being relatively slow, it eats up most of the running time of the metaheuristic and, thus, its speed plays significant role in performance of the metaheuristic. Despite its importance, little attention has been paid in the literature to efficient implementation of schedule generation schemes. We give detailed description of serial schedule generation scheme, including new improvements, and propose a new approach for speeding it up, by using Bloom filters. The results are further strengthened by automated control of parameters. Finally, we employ online algorithm selection to dynamically choose which of the two implementations to use. This hybrid approach significantly outperforms conventional implementation on a wide range of instances.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 03 Jan 2018 11:17
Last Modified: 03 Jan 2018 11:17
URI: http://repository.essex.ac.uk/id/eprint/20961

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