While the EA-inspired approach offers several advantages for enhancing Vision Transformers, there are potential limitations and drawbacks that should be considered:
Complexity and Computational Cost: The incorporation of evolutionary principles into neural network architectures can increase the complexity of the models and require additional computational resources. This may lead to longer training times and higher computational costs, especially for large-scale models.
Hyperparameter Sensitivity: EA-inspired techniques often involve tuning various hyperparameters, such as mutation rates, crossover probabilities, and population sizes. Finding the optimal set of hyperparameters can be challenging and time-consuming, potentially hindering the scalability and applicability of the approach.
Limited Interpretability: The black-box nature of some EA-inspired techniques may limit the interpretability of the resulting neural network models. Understanding the inner workings and decision-making processes of these models can be challenging, especially in complex architectures.
To address these limitations in future work, researchers can focus on developing more efficient and scalable EA-inspired algorithms tailored to specific neural network architectures. Additionally, efforts can be made to automate the hyperparameter tuning process and improve the interpretability of models through advanced visualization and explanation techniques.