'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research'
Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial I...
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| Format: | Conference or Workshop Item |
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2004
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| Online Access: | https://eprints.nottingham.ac.uk/334/ |
| _version_ | 1848790393786728448 |
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| author | Aickelin, Uwe |
| author_facet | Aickelin, Uwe |
| author_sort | Aickelin, Uwe |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here |
| first_indexed | 2025-11-14T18:11:54Z |
| format | Conference or Workshop Item |
| id | nottingham-334 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:11:54Z |
| publishDate | 2004 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-3342020-05-04T20:31:14Z https://eprints.nottingham.ac.uk/334/ 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' Aickelin, Uwe Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a step-by-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here 2004 Conference or Workshop Item PeerReviewed Aickelin, Uwe (2004) 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research'. In: Annual Operational Research Conference 46, 7-9 September 2004, York, UK. |
| spellingShingle | Aickelin, Uwe 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title | 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title_full | 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title_fullStr | 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title_full_unstemmed | 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title_short | 'Artificial Immune Systems: A new Decision Making Paradigm for Operational Research' |
| title_sort | 'artificial immune systems: a new decision making paradigm for operational research' |
| url | https://eprints.nottingham.ac.uk/334/ |