TL;DR
A new memetic differential evolution (DE) algorithm with a niching strategy and a Gaussian distribution-based local search to effectively explore and exploit the CNN architectural search space for image classification tasks, named MDE/NS is introduced.
It is demonstrated that hyperparameter optimization dynamics depend heavily on dataset complexity, where computational efficiency is the primary differentiator for simpler classification tasks, but optimization architecture selection becomes critical for navigating challenging medical imaging applications.
An improved multi-operator differential evolution algorithm, called NN-IMODE, tailored for comprehensive Feedforward Neural Networks optimization, encompassing the number of hidden layers, neurons per layer, weights, biases, and activation functions, is introduced.
It is demonstrated that network architecture and its coeficients can be learned together by unifying concepts of evolutionary search within a population based traditional training process.
A novel adaptive fusion framework that adaptively combines CNN and Transformer features through learnable gating, attention-based feature integration, and explainable-AI methods is developed, intended to improve both computational efficiency and model interpretability.
A dynamic pruning method that uses reinforcement learning guided by a dynamic reward function to automatically explore the pruning rate of each layer, and uses the designed similarity-based connected graph (SCG) mechanism to perform channel removal operations during training.
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