Abstract
The self-organising neural network with weight normalisation (SONN-WN) for solving combinatorial optimisation problems (COPs) is investigated in terms of its performance and dynamical characteristics. A simplified computational model of the weight normalisation process is constructed, which reveals symmetry-breaking bifurcations in a typical node outside the winning neighbourhood. Experimental results with the N-queen problem show that bifurcations can enhance solution qualities in a consistent manner. A mechanism based on the weights’ transient trajectories is proposed to account for the neural network’s capacity to escape local minima.
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Kwok, T., Smith, K.A. (2003). Performance-Enhancing Bifurcations in a Self-organising Neural Network. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_50
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DOI: https://doi.org/10.1007/3-540-44868-3_50
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