In the last years, inspired by the fact that natural brains themselves are the products of an evolutionary process, the quest for evolving and optimizing artificial neural networks through evolutionary computation has enabled researchers to successfully apply neuroevolution to many domains such as strategy games, robotics, big data, and so on. The reason behind this success lies in important capabilities that are typically unavailable to traditional approaches, including evolving neural network building blocks, hyperparameters, architectures, and even the algorithms for learning themselves (meta-learning).
Although promising, the use of neuroevolution poses important problems and challenges for its future development.
Firstly, many of its paradigms suffer from a lack of parameter-space diversity, meaning a failure to provide diversity in the behaviors generated by the different networks.
Moreover, harnessing neuroevolution to optimize deep neural networks requires noticeable computational power and, consequently, investigating new trends in enhancing computational performance.

A closely related and rapidly growing field is Neural Architecture Search (NAS), which aims to automatically design high-performing neural network architectures by employing techniques from evolutionary computation and reinforcement learning. NAS methods strictly rely on neuroevolution principles to explore the vast space of possible neural network configurations and discover novel, efficient architectures. The tight coupling between neuroevolution and NAS highlights their synergistic relationship, with advancements in one field enabling progress in the other.
Although promising, the use of neuroevolution and NAS poses important problems and challenges for their future development.
Firstly, many of their paradigms suffer from a lack of parameter-space diversity, meaning a failure to provide diversity in the behaviours generated by the different networks.
Moreover, harnessing neuroevolution and NAS to optimize deep neural networks requires noticeable computational power and, consequently, the investigation of new trends in enhancing computational performance.
This workshop aims:
- to bring together researchers working in the fields of deep learning, evolutionary computation, and optimization to exchange new ideas about potential directions for future research;
- to create a forum of excellence on neuroevolution that will help interested researchers from various areas, ranging from computer scientists and engineers on the one hand to application-devoted researchers on the other hand, to gain a high-level view of the current state of the art.archers on the other hand, to gain a high-level view about the current state of the art.

Since an increasing trend to neuroevolution and NAS in the next few years seems likely to be observed, not only will a workshop on this topic be of immediate relevance to get insight into future trends, but it will also provide a common ground to encourage novel paradigms and applications. Therefore, researchers emphasizing neuroevolution and NAS issues in their work are encouraged to submit their work. This event is also ideal for informal contacts, exchanging ideas, and discussions with fellow researchers.
The scope of the workshop is to receive high-quality contributions on topics related to neuroevolution and neural architecture search, ranging from theoretical works to innovative applications in the context of (but not limited to):
• theoretical and experimental studies involving neuroevolution and NAS on machine learning in general, and deep and reinforcement learning in particular
• development of innovative neuroevolution and NAS paradigms
• parallel and distributed neuroevolution and NAS methods
• new search operators for neuroevolution and NAS
• hybrid methods for neuroevolution and NAS
• surrogate models for fitness estimation in neuroevolution and NAS
• adopt evolutionary multi-objective and many-objective optimisation techniques in neuroevolution and NAS
• propose new benchmark problems for neuroevolution and NAS
• applications of neuroevolution and NAS to Artificial Intelligence agents and to real-world problems.