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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, the investigation of new trends in enhancing computational performance.

NEWK@Work 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 in the next 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 putting emphasis on neuroevolution 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, ranging from theoretical works to innovative applications in the context of (but not limited to):
- theoretical and experimental studies involving neuroevolution on machine learning in general, and on deep and reinforcement learning in particular
- development of innovative neuroevolution paradigms
- parallel and distributed neuroevolution methods
- new search operators for neuroevolution
- hybrid methods for neuroevolution
- surrogate models for fitness estimation in neuroevolution
- adopt evolutionary multi-objective and many-objective optimisation techniques in neuroevolution
- propose new benchmark problems for neuroevolution
- applications of neuroevolution to Artificial Intelligence agents and to real-world problems.

Submission and important dates

Submission opening: February 12, 2024

Paper Submission deadline: April 8, 2024
Notification of paper acceptance: May 3, 2024 
Camera ready submission: May 10, 2024
Author's mandatory registration: date to be confirmed
Conference dates: July 14-18, 2024

General information on GECCO workshops can be found at


Authors must submit their papers using the GECCO submission site at
Submissions should adhere to the ACM SIG guidelines as GECCO’s full papers:
Papers Submission Instructions.

Each paper submitted will be rigorously evaluated in a double-blind review process. The evaluation will ensure high interest and expertise of the reviewers. Review criteria include significance of the work, technical soundness, novelty, clarity, writing quality, and sufficiency of information to permit replication, if applicable. All accepted papers will be published in the ACM Digital Library.

Program Committee

Takaya Arita, Nagoya University, Japan
Shaveta Arora, NCU Gurgaon, India
Sebastian Basterrech, Technical University of Ostrava, Czech Republic
Peter Bentley, University College London, UK
Ying Bi, Victoria University of Wellington, New Zealand
Andrés Camero Unzueta, University of Munich, Germany
Anders Lyhne Christensen, University of Southern Denmark, Denmark
Victor Costa, University of Coimbra, Portugal
Federico Divina, Pablo de Olavide University, Spain
Marcio Dorn, Federal University of Rio Grande do Sul, Brazil
Steffen Finck, FH Vorarlberg University of Applied Sciences, Austria
Marcus Gallagher, University of Queensland, Australia
Jin-Kao Hao, University of Angers, France
Colin Graeme Johnson, University of Nottingham, UK
Risto Miikkulainen, The University of Texas, USA
Yukai Qiao, University of Queensland, Australia
Paulo Alexandro Ribeiro Cortez, University of Minho, Portugal
Patricia Ruiz, University of Cadiz, Spain
Catherine D. Schuman, University of Tennessee, USA
Eric Scott, George Mason University, USA
Fergal Stapleton, Maynooth University, Ireland
Thomas Stibor, GSI Helmholtz Centre for Heavy Ion Research, Germany
Catalin Stoean, University of Craiova, Romania
Renato Tinós, University of São Paulo, Brazil
Leonardo Trujillo, Instituto Tecnológico de Tijuana, Mexico
Liqiang Wang, University of Central Florida, USA

GECCO'24 @ Melbourne (hybrid)

GECCO 2024 will be held in hybrid mode, with all events facilitating online participation.

Organized by

Ivanoe De Falco

Antonio Della Cioppa
University of Salerno, ITALY

Edgar Galvan
Maynooth University, IRELAND

Ernesto Tarantino

Umberto Scafuri

Mengjie Zhang
Victoria University of Wellington, NEW ZEALAND

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