ACM Transactions on Evolutionary Learning and Optimization

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	5703

Journal Citation Indicator

Year	Value
2024	212

SJR (SCImago Journal Rank)

Year	Value
2024	0.899

Quartile

Year	Value
2024	Q1

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization

  Citation: 88

  Authors: Mickaël, Nathan

• Greed Is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation

  Citation: 75

  Authors: George, Richard M., Alma A. M., Jonathan E.

• Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview

  Citation: 45

  Authors: Florian, Tobias, Julia, Florian, Stefan, Martin, Lennart, Janek, Jakob, Michel, Eduardo C., Juergen, Bernd

• IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

  Citation: 42

  Authors: Hao, Diederick, Furong, Carola, Thomas

• Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey

  Citation: 29

  Authors: Olivier

• Reproducibility in Evolutionary Computation

  Citation: 29

  Authors: Manuel, Juergen, Luís

• A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

  Citation: 28

  Authors: Benjamin, Frank

• Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization

  Citation: 24

  Authors: Peilan, Wenjian, Xin, Jiajia, Yingying, Xuan

• A Learning-based <i>Innovized</i> Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization

  Citation: 20

  Authors: Sukrit, Dhish Kumar, Kalyanmoy, Erik D.

• Spatial Coevolution for Generative Adversarial Network Training

  Citation: 18

  Authors: Erik, Jamal, Abdullah, Tom, Una-May

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