Ma, Yunfeng and Indrusiak, Leandro Soares orcid.org/0000-0002-9938-2920 (2016) Hardware-accelerated parallel genetic algorithm for fitness functions with variable execution times. In: GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference. 2016 Genetic and Evolutionary Computation Conference, GECCO 2016, 20-24 Jul 2016 ACM , USA , pp. 829-836.
Abstract
Genetic Algorithms (GAs) following a parallel master-slave architecture can be effectively used to reduce searching time when fitness functions have fixed execution time. This paper presents a parallel GA architecture along with two accelerated GA operators to enhance the performance of master-slave GAs, specially when considering fitness functions with variable execution times. We explore the performance of the proposed approach, and analyse its effectiveness against the state-of-the-art. The results show a significant improvement in search times and fitness function utilisation, thus potentially enabling the use of this approach as a faster searching tool for timing-sensitive optimisation processes such as those found in dynamic real-time systems.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Genetic algorithms,Hardware realization,Parallelization,Speedup technique,Time-tabling and scheduling |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 21 Sep 2016 10:52 |
Last Modified: | 06 Feb 2025 00:03 |
Published Version: | https://doi.org/10.1145/2908812.2908879 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/2908812.2908879 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:104865 |