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dc.contributor.author Mathotaarachchi, MSS
dc.contributor.author Perera, DC
dc.contributor.author Udawatte, L
dc.contributor.author Perera, S
dc.contributor.editor Weerawardhana, S
dc.contributor.editor Madusanka, A
dc.contributor.editor Dilrukshi, T
dc.contributor.editor Aravinda, H
dc.date.accessioned 2022-12-05T06:42:23Z
dc.date.available 2022-12-05T06:42:23Z
dc.date.issued 2011-11
dc.identifier.citation ****** en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19664
dc.description.abstract A novel method for selecting the appropriate architecture and learning rule of an artificial neural network for a given application is discussed in this paper. Evolutionary Artificial Neural Networks (EANN) use the adaptation capabilities of genetic algorithms in which the natural selection process is used to attain the optimum network structure and learning algorithm for a specific task. ANNEbot is a framework which allows the combined powers of learning and adaptation of EANNs to be applied in various machine learning tasks. The framework was tested on the Iris Classification problem and the Wisconsin Breast Cancer Diagnosis problem, both of which provided results with above 90% accuracy. ANNEbot was also successfully applied on a robotic application for obstacle avoidance. en_US
dc.language.iso en en_US
dc.publisher Computer Science & Engineering Society c/o Department of Computer Science and Engineering, University of Moratuwa. en_US
dc.subject Evolutionary artificial neural networks en_US
dc.subject GA en_US
dc.subject ANN en_US
dc.title Annebot – an evolutionary artificial neural network framework. en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.identifier.year 2011 en_US
dc.identifier.conference CS & ES Conference 2011 en_US
dc.identifier.place Moratuwa. Sri Lanka en_US
dc.identifier.proceeding Proceedings of the CS & ES Conference 2011 en_US


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