Hardware-accelerated parallel genetic algorithm for fitness functions with variable execution times

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Title of host publicationGECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
DatePublished - 20 Jul 2016
Pages829-836
Number of pages8
PublisherAssociation for Computing Machinery, Inc
Original languageEnglish
ISBN (Print)9781450342063

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.

    Research areas

  • Genetic algorithms, Hardware realization, Parallelization, Speedup technique, Time-tabling and scheduling

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