Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

Genetic algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multi-and many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a fe...

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Bibliographic Details
Published in:Telkomnika Vol. 19; no. 1; pp. 182 - 191
Main Authors: Sugiarto, Indar, Furber, Steve
Format: Journal Article
Language:English
Published: Yogyakarta Ahmad Dahlan University 01.02.2021
Subjects:
Communication
Distributed processing
Feasibility studies
Genetic algorithms
Heuristic methods
Microprocessors
Neighborhoods
Neural networks
Optimization
Power
Power consumption
ISSN:1693-6930, 2302-9293
Online Access:Get full text
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Summary:Genetic algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multi-and many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker.
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ISSN:1693-6930
2302-9293
DOI:10.12928/telkomnika.v19i1.15026