This study explores optimizing hyperparameters in Generative Adversarial Networks (GANs) using the gaussian analytical hierarchy process (gaussian AHP). By integrating machine learning techniques and multi-criteria de...
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This study explores optimizing hyperparameters in Generative Adversarial Networks (GANs) using the gaussian analytical hierarchy process (gaussian AHP). By integrating machine learning techniques and multi-criteria decision methods, the aim is to enhance the performance and efficiency of GAN models. It trains GAN models using the Fashion MNIST dataset. It applies gaussian AHP to optimize hyperparameters based on multiple performance criteria, such as the quality of generated images, training stability, and training time. Iterative experiments validate the methodology by automatically adjusting hyperparameters based on the obtained scores, thereby maximizing the model's efficiency and quality. Results indicate significant improvements in image generation quality and computational efficiency. The study highlights the effectiveness of combining gaussian AHP with GANs for systematic hyperparameter optimization, providing insights into achieving higher performance in image generation tasks. Future research could extend this approach to other neural network architectures and diverse datasets, further demonstrating the versatility of this optimization technique. This method's potential applications extend across various domains, including data augmentation and anomaly detection, indicating its broad applicability and impact.
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