GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks

GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks

Generative Adversarial Networks (GANs) are highly effective for generating realistic images, yet their training can be unstable due to challenges such as mode collapse and oscillatory convergence. In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) t...

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Bibliographic Details
Main Authors: Despoina Konstantopoulou, Paraskevi Zacharia, Michail Papoutsidakis, Helen C. Leligou, Charalampos Patrikakis
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Algorithms
Subjects:
image generation
generative AI
genetic algorithms
deep convolutional generative adversarial networks
discriminator weight optimization
Online Access:https://www.mdpi.com/1999-4893/17/12/584
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