Detecting change and dealing with uncertainty in imperfect evolutionary environments

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

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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.

Item Type: Article
Keywords: Artificial Intelligence, Evolutionary Computation, Imperfect Evolutionary Systems, Particle Swarm optimization, Learning
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Ozcan, Dr Ender
Date Deposited: 09 Mar 2016 15:32
Last Modified: 04 May 2020 17:05

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