#5776. Fly visual evolutionary neural network solving large-scale global optimization

July 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Theoretical Computer Science;
Human-Computer Interaction;
Artificial Intelligence;
Software;
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Abstract:
Neurophysiologic achievements claimed that the fly visual system could naturally contribute to a type of artificial computation model which used motion-sensitive neurons to detect the local movement direction changes of moving objects. It, however, still remains open how the neurons information-processing mechanisms and the inspirations of swarm intelligence can be integrated to serve an interdisciplinary topic between computer vision and intelligence optimization-visual evolutionary neural networks. Hereby, a fly visual evolutionary neural network is developed to solve large-scale global optimization (LSGO), inspired by swarm evolution and the characteristics of fly visual perception. It includes two functional modules, of which one is to generate global and local motion direction activities of visual neural nodes, and the other takes the activities as learning rates to update the nodes states by a population-like evolutionary strategy. Also, it is used to optimize the structure of a multilayer perceptron to acquire a sample classification model.
Keywords:
convergence; fly visual neural network; LSGO; multilayer perceptron; swarm intelligence

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