# Improving Automated Machine-Learning Systems through Green AI
## 2 State of the Art  
The widespread adoption of artificial intelligence (AI) has amplified the importance of achieving a delicate equilibrium between machine-learning model performance and sustainability. Building upon these principles, this is primarily due to the undeniable impact AI systems now exert on our carbon footprint [25; 26]. Coping with this challenge has prompted the exploration of diverse strategies. In the pursuit of this equilibrium, these encompass the curation of more efficient models [27], the meticulous optimization of hyperparameters [28], and the judicious utilization of knowledge transfer [29]. Additionally, the incorporation of reinforcement learning techniques [30], real-time monitoring mechanisms [31], and energy-efficient hardware [32] has emerged as a common practice. Within this multifaceted landscape, particular attention has been dedicated to life cycle assessments [33] and the design of efficient datasets [34]. In a complementary vein, these facets have been accentuated, recognizing the pivotal roles that awareness and education play in the promotion of sustainability within the machine-learning domain [11]. Hyperparameter optimization for machine-learning algorithms has been a hub of active research, notably witnessing substantial progress in recent works [35; 36; 37; 38]. In response to the growing need for environmentally friendly AI, various optimization strategies have arisen [22; 39; 40]. These strategies align with Green AI principles and strive to mitigate the environmental repercussions associated with AI development. Among the most resource-efficient optimization techniques are those based on Bayesian sequential models and their derivatives [41; 42; 43]. Specifically, they aim to minimize the number of objective function evaluations, thus reducing computational expenses. Bayesian optimization, in particular, leverages probabilistic surrogate models to guide the selection of hyperparameters to evaluate based on prior results [44]. Although most current research centers on carbon footprint monitoring, hyperparameter fine-tuning, and model benchmarking [39], other innovative approaches have sought to reduce the number of iterations needed to find optimal hyperparameters. These strategies, such as those outlined by [PERSON] et al. [45], represent a significant departure from traditional hyperparameter tuning and can substantially diminish the energy costs associated with searching for the optimal set of hyperparameters. Notably, [PERSON] et al. [46] demonstrated how hyperparameter tuning, when inclusive of energy consumption considerations, can foster the development of more energy-efficient models. Variational Autoencoders (VAEs) have garnered significant attention due to their potential for crafting compact and lightweight neural networks while incurring minimal loss in accuracy [47]. The utilization of VAEs enables model compression, therefore reducing computational demands and energy consumption [48]. Moreover, VAEs excel in transferring knowledge from extensive teacher networks to more economical student models [29; 49]. In a recent study by [PERSON] et al. [50], a comparative evaluation of various VAE variations was conducted, with a specific emphasis on analyzing the energy efficiency of different models. This research aligns with the principles of Green AI, highlighting the need for enhanced metrics beyond FLOPs (floating-point operations per second) and improved calculation methodologies. Regarding deep learning models, such as VAEs and Convolutional Variational Autoencoders (ConvVAEs), it is possible to observe slightly divergent outcomes. These variations can be attributed to several contributing factors, as discussed in [PERSON] et al. [51]. In the context of ConvVAEs, these outcomes are influenced by factors such as weight initialization, network architecture, and training hyperparameters. These considerations underscore the critical importance of precise configuration and hyperparameter optimization [42].