Learning enhancement of three-term backpropagation network based on elitist multi-objective evolutionary algorithms
The pattern classification problem in machine learning algorithms is the task of assigning objects to one of a different predefined group of categories related to that object. Among the successful machine learning methods are Artificial Neural Networks (ANNs), which aim to minimize the error rate of...
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| Format: | Thesis |
| Language: | English |
| Published: |
2015
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| Online Access: | http://eprints.utm.my/id/eprint/54893/1/AshrafOsmanIbrahimPFC2015.pdf http://eprints.utm.my/id/eprint/54893/ http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:94640 |
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| Summary: | The pattern classification problem in machine learning algorithms is the task of assigning objects to one of a different predefined group of categories related to that object. Among the successful machine learning methods are Artificial Neural Networks (ANNs), which aim to minimize the error rate of the training data and generate a simple network architecture to obtain a high classification accuracy. However, designing the ANN architecture is difficult due to the complexity of the structure, such as the network structure, number of hidden nodes and adjustment of weights. Therefore, a number of Evolutionary Algorithms (EAs) has been proposed to improve these network complexities. These algorithms are meant to optimize the connection weight, network structure, network error rate and classification accuracy. Nevertheless, these algorithms are implemented to optimize only one objective, despite the importance of executing many objectives simultaneously. Therefore, this study proposes simultaneous learning and structure optimization for designing a Three-term Backpropagation (TBP) network with four variants of Elitist Multi-objective Evolutionary Algorithms (EMOEAs). These include the Elitist Multi-objective Genetic Algorithm (EMOGA), Hybrid Elitist Multi-objective Genetic Algorithm (HEMOGA), Memetic Adaptive Elitist Multi-objective Genetic Algorithm (MAEMOGA) and the Elitist Multi-objective Differential Evolution (EMODE). The proposed methods are developed to evolve towards a Pareto-optimal set that is defined by multi-objective optimization consisting of connection weight, error rate and structural complexity of the network. The proposed methods are tested on binary and multi-class pattern classification problems. The results show that the proposed MAEMOGA and EMODE are better than EMOGA and HEMOGA in obtaining simple network structure and classification accuracy. |
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