Victoria University

Problem Decomposition and Adaptation in Cooperative Neuro-Evolution

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dc.contributor.advisor Frean, Marcus
dc.contributor.advisor Zhang, Mengjie Chandra, Rohitash 2012-04-11T00:38:50Z 2012-04-11T00:38:50Z 2012 2012
dc.description.abstract One way to train neural networks is to use evolutionary algorithms such as cooperative coevolution - a method that decomposes the network's learnable parameters into subsets, called subcomponents. Cooperative coevolution gains advantage over other methods by evolving particular subcomponents independently from the rest of the network. Its success depends strongly on how the problem decomposition is carried out. This thesis suggests new forms of problem decomposition, based on a novel and intuitive choice of modularity, and examines in detail at what stage and to what extent the different decomposition methods should be used. The new methods are evaluated by training feedforward networks to solve pattern classification tasks, and by training recurrent networks to solve grammatical inference problems. Efficient problem decomposition methods group interacting variables into the same subcomponents. We examine the methods from the literature and provide an analysis of the nature of the neural network optimization problem in terms of interacting variables. We then present a novel problem decomposition method that groups interacting variables and that can be generalized to neural networks with more than a single hidden layer. We then incorporate local search into cooperative neuro-evolution. We present a memetic cooperative coevolution method that takes into account the cost of employing local search across several sub-populations. The optimisation process changes during evolution in terms of diversity and interacting variables. To address this, we examine the adaptation of the problem decomposition method during the evolutionary process. The results in this thesis show that the proposed methods improve performance in terms of optimization time, scalability and robustness. As a further test, we apply the problem decomposition and adaptive cooperative coevolution methods for training recurrent neural networks on chaotic time series problems. The proposed methods show better performance in terms of accuracy and robustness. en_NZ
dc.language.iso en_NZ
dc.publisher Victoria University of Wellington en_NZ
dc.subject Neural networks en_NZ
dc.subject Cooperative coevolution en_NZ
dc.subject Recurrent network en_NZ
dc.subject Co-operative co-evolution en_NZ
dc.title Problem Decomposition and Adaptation in Cooperative Neuro-Evolution en_NZ
dc.type Text en_NZ
vuwschema.contributor.unit School of Engineering and Computer Science en_NZ
vuwschema.subject.marsden 280212 Neural Networks, Genetic Algorithms and Fuzzy Logic en_NZ
vuwschema.subject.marsden 280207 Pattern Recognition en_NZ
vuwschema.type.vuw Awarded Doctoral Thesis en_NZ Computer Science en_NZ Victoria University of Wellington en_NZ Doctoral en_NZ Doctor of Philosophy en_NZ
vuwschema.subject.anzsrcfor 089999 Information and Computing Sciences not elsewhere classified en_NZ

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