Table of Contents


Machine learning for optimization has attracted significant attention in both the machine learning and operations research communities. Novel machine learning techniques have been developed to effectively solve high-dimensional and complex optimization problems. These include automatic learning of heuristics, direct prediction of high-quality solutions, learning to branch for branch-and-bound algorithms, and learning to reduce or decompose optimization problems. Conversely, population-based metaheuristics in general, and evolutionary algorithms in particular, have also been used for high-dimensional learning tasks. Neuro-evolution for instance has shown promising results in tackling complex supervised and reinforcement learning problems. The aim of this workshop is to explore the synergy between machine learning and evolutionary algorithms to tackle high-dimensional optimization and learning problems. The workshop broadly covers novel techniques to enhance evolutionary algorithms via machine learning for solving complex large-scale optimization problems and/or novel algorithmic advancements of population-based metaheuristics for solving high-dimensional learning problems.


Potential topics may include (but not limited to):

• Automatic meta-heuristic design using machine learning (or hyper-heuristic),

• Predicting high-quality solutions to warm-start evolutionary algorithms,

• Predicting unknown parameters for optimization problems via machine learning,

• Algorithm selection using machine learning,

• Problem structure learning,

• Surrogate models for expensive optimization problems,

• Neural architecture search using evolutionary methods,

• Deep Neuro-Evolution.

Paper Submission

Manuscripts should be prepared according to the standard format and page limit of regular papers specified for GECCO’2022. Instructions on the preparation of the manuscripts can be obtained at the GECCO’2022 website:

Please see

As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (, including ACM’s new Publications Policy on Research Involving Human Participants and Subjects (

Deadline: April 11 2022

Important Dates

• Submission opening: February 11, 2022
• Submission deadline: April 11, 2022
• Notification: April 25, 2022
• Camera-ready: May 4, 2022
• Workshop date: TBD


Nabi Omidvar is a University Academic Fellow (Assistant Professor) with the School of Computing, University of Leeds, and Leeds University Business School, UK. He is an expert in large-scale global optimization and is currently a senior member of the IEEE and the chair of IEEE Computational Intelligence Society’s Taskforce on Large-Scale Global Optimization. He has made several award-winning contributions to the field including the state-of-the-art variable interaction analysis algorithm which won the IEEE Computational Intelligence Society’s best paper award in 2017. He also coauthored a paper which won the large-scale global optimization competition in the IEEE Congress on Evolutionary Computation in 2019. Dr. Omidvar’s current research interests are high-dimensional (deep) learning and the applications of artificial intelligence in financial services.

Yuan Sun is a Research Fellow in the School of Computing and Information Systems, University of Melbourne and the Vice-Chair of the IEEE CIS Taskforce on Large-Scale Global Optimization. He completed his Ph.D degree from University of Melbourne and a Bachelor’s degree from Peking University. His research interests include artificial intelligence, evolutionary computation, operations research, and machine learning. He has published more than twenty research papers in these areas, and his research has been nominated for the best paper award at GECCO 2020 and won the CEC 2019 Competition on Large-Scale Global Optimization.

Xiaodong Li received his B.Sc. degree from Xidian University, Xi’an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Computing Technologies, RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, data analytics, multiobjective optimization, multimodal optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a former vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS “IEEE Transactions on Evolutionary Computation Outstanding Paper Award”. He is an IEEE Fellow.