Deng, Y;
Bentley, PJ;
Momshad, A;
(2017)
Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures.
In:
2016 IEEE Symposium Series on Computational Intelligence (SSCI).
IEEE
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Abstract
Biological systems have become highly significant for traditional computer architectures as examples of highly complex self-organizing systems that perform tasks in parallel with no centralized control. However, few researchers have compared the suitability of different computing approaches for the unique features of Artificial Immune Systems (AIS) when trying to introduce novel computing architectures, and few consider the practicality of their solutions for real world machine learning problems. We propose that the efficacy of AIS-based computing for tackling real world datasets can be improved by the exploitation of intrinsic features of computer architectures. This paper reviews and evaluates current existing implementation solutions for AIS on different computing paradigms and introduces the idea of “C Principles” and “A Principles”. Three Artificial Immune Systems implemented on different architectures are compared using these principles to examine the possibility of improving AIS through taking advantage of intrinsic hardware features.
| Type: | Proceedings paper |
|---|---|
| Title: | Improving Artificial-Immune-System-based computing by exploiting intrinsic features of computer architectures |
| Event: | 2016 IEEE Symposium Series on Computational Intelligence |
| Location: | Athens, Greece |
| Dates: | 06 December 2016 - 09 December 2016 |
| ISBN: | 9781509042401 |
| ISBN-13: | 9781509042418 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SSCI.2016.7850157 |
| Publisher version: | http://dx.doi.org/10.1109/SSCI.2016.7850157 |
| Language: | English |
| Additional information: | © 2016 IEEE. This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, Artificial Immune Systems, Systemic Computing, Multi-threaded Computing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1550623 |
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