The ACM Transactions on Evolutionary Learning and Optimization will publish high quality original papers in all areas of evolutionary computation and related areas such as population-based methods, Bayesian optimization, or swarm intelligence.

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.