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Evolving weighted topologies for neural networks using genetic programming

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NeuroEvolution of Augmenting Topologies (NEAT) is a method of evolving weighted topologies for neural networks, which has been showed to be effective in solving double pole balancing problems. Its usability, however, is largely reduced by its high specificity and custom architecture. In this paper we propose much more standardized framework for evolving neural networks, achieving comparable or even better performance in three benchmark problems. We show that tree-based genetic programming (GP) requires only minor modifications to represent and evolve neural networks. Despite its high performance, however, we remain sceptical about the robustness of the proposed method. In a series of further experiments we demonstrate the performance is sensitive to parameter settings and algorithm details. Therefore we refrain from making conclusions about performance of our method and only stress the improvement regarding its simplicity.
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Bibliogr. 12 poz., rys.
  • Szczecin University of Technology, Faculty of Computer Science and Information Technology
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