The ACM Transactions on Evolutionary Learning and Optimization publishes original papers in all areas of evolutionary computation and related areas such as evolutionary machine learning, evolutionary reinforcement learning, Bayesian optimization, evolutionary robotics and other metaheuristics.
We welcome papers that make solid contributions to theory, method and applications. Relevant domains include continuous, combinatorial or multi-objective optimization. Applications of interest include but are not limited to logistics, scheduling, healthcare, games, robotics, software engineering, feature selection, clustering as well as the open-ended evolution of complex systems.
We are particularly interested in papers at the intersection of optimization and machine learning, such as the use of evolutionary optimization for tuning and configuring machine learning algorithms, machine learning to support and configure evolutionary optimization, and hybrids of evolutionary algorithms with other optimization and machine learning techniques. ACM TELO encourages reproducibility.
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ACM Updates Its Peer Review Policy
ACM is pleased to announce that its Publications Board has approved an updated Peer Review Policy. If you have any questions regarding the update, the associated FAQ addresses topics such as confidentiality, the use of large language models in the peer review process, conflicts of interest, and several other relevant concerns. If there are any issues that are not addressed in the FAQ, please contact ACM’s Director of Publications, Scott Delman.
New ACM Policy on Authorship
ACM has a new Policy on Authorship, covering a range of key topics, including the use of generative AI tools. Please familiarize yourself with the new policy and the associated list of Frequently Asked Questions.
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