Self-Organizing Maps
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Maximum Likelihood Topographic Map Formation
Neural Computation
On Self-Organizing Feature Map (SOFM) Formation by Direct Optimization Through a Genetic Algorithm
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Cooperative information maximization with Gaussian activation functions for self-organizing maps
IEEE Transactions on Neural Networks
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
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Recently, the formation of topographic maps has been approached via a direct optimization strategy involving the use of heuristic search techniques. In this paper, we move a step further in this line of research by devising and empirically assessing the performance of six different evolutionary algorithms (EA) towards the automatic generation of high-quality maps. Besides, we also report an analysis over the convexity profiles exhibited by different realizations of the adopted cost function in a manner as to testify its inherent search complexity. The simulation results reveal that, although the EA schemes do not distinguish so much in terms of the average quality of the maps they form, there is sometimes a significant difference of performance in terms of robustness (variance of the quality indices) and efficiency (number of iterations to converge to a good solution).