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 par- allelising their core processes. This paper describes a feasibility study of implement- ing 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 over- head. However, due to its small packets distribution mechanism and constrained pro- cessing 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 benchmark- ing test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker.
|Number of pages||10|
|Journal||TELKOMNIKA Telecommunications, Computing, Electronics and Control|
|Publication status||Published - 1 Feb 2021|
- Genetic algorithm
- Network on chip
- Neuromorphic computing
- Parallel computing