Development of a Platform-Oriented Algorithm for Synthesizing an Optimal Neural Network Architecture for Monitoring Laser Polishing of Quartz Glass

An evolutionary synthesis algorithm for neural network architectures is proposed. It is designed to address real-time process monitoring tasks with limited computing resources and training data. The algorithm is based on a modified neuroevolution approach, in which the individual genome comprises a graph of large functional blocks and the history of innovations is tracked. The evaluation on population individuals was performed by the multi-criteria algorithm NSGA-II based on classification accuracy and the forward pass time network on the target device. The algorithm’s performance is demonstrated on an image classification task using the CIFAR-10 dataset, as well as on the applied task of classifying operating modes during the single-beam laser polishing of quartz glass.

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