Detecting change and dealing with uncertainty in imperfect evolutionary environments
Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants....
| Main Authors: | , , , |
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| Format: | Article |
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Elsevier
2015
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| Online Access: | https://eprints.nottingham.ac.uk/31172/ |
| _version_ | 1848794141947854848 |
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| author | Mujtaba, Hasan Kendall, Graham Baig, Abdul R. Özcan, Ender |
| author_facet | Mujtaba, Hasan Kendall, Graham Baig, Abdul R. Özcan, Ender |
| author_sort | Mujtaba, Hasan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high. |
| first_indexed | 2025-11-14T19:11:29Z |
| format | Article |
| id | nottingham-31172 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:11:29Z |
| publishDate | 2015 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-311722020-05-04T17:05:28Z https://eprints.nottingham.ac.uk/31172/ Detecting change and dealing with uncertainty in imperfect evolutionary environments Mujtaba, Hasan Kendall, Graham Baig, Abdul R. Özcan, Ender Imperfection of information is a part of our daily life; however, it is usually ignored in learning based on evolutionary approaches. In this paper we develop an Imperfect Evolutionary System that provides an uncertain and chaotic imperfect environment that presents new challenges to its habitants. We then propose an intelligent methodology which is capable of learning in such environments. Detecting changes and adapting to the new environment is crucial to exploring the search space and exploiting any new opportunities that may arise. To deal with these uncertain and challenging environments, we propose a novel change detection strategy based on a Particle Swarm Optimization system which is hybridized with an Artificial Neural Network. This approach maintains a balance between exploitation and exploration during the search process. A comparison of approaches using different Particle Swarm Optimization algorithms show that the ability of our learning approach to detect changes and adapt as per the new demands of the environment is high. Elsevier 2015-05-01 Article PeerReviewed Mujtaba, Hasan, Kendall, Graham, Baig, Abdul R. and Özcan, Ender (2015) Detecting change and dealing with uncertainty in imperfect evolutionary environments. Information Sciences, 302 . pp. 33-49. ISSN 1872-6291 Artificial Intelligence Evolutionary Computation Imperfect Evolutionary Systems Particle Swarm optimization Learning http://www.sciencedirect.com/science/article/pii/S0020025515000055 doi:10.1016/j.ins.2014.12.053 doi:10.1016/j.ins.2014.12.053 |
| spellingShingle | Artificial Intelligence Evolutionary Computation Imperfect Evolutionary Systems Particle Swarm optimization Learning Mujtaba, Hasan Kendall, Graham Baig, Abdul R. Özcan, Ender Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title | Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title_full | Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title_fullStr | Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title_full_unstemmed | Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title_short | Detecting change and dealing with uncertainty in imperfect evolutionary environments |
| title_sort | detecting change and dealing with uncertainty in imperfect evolutionary environments |
| topic | Artificial Intelligence Evolutionary Computation Imperfect Evolutionary Systems Particle Swarm optimization Learning |
| url | https://eprints.nottingham.ac.uk/31172/ https://eprints.nottingham.ac.uk/31172/ https://eprints.nottingham.ac.uk/31172/ |