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ACM Transactions on Evolutionary Learning and Optimization is a peer-reviewed journal published by ACM, covering Advanced Multi-Objective Optimization Algorithms, Metaheuristic Optimization Algorithms Research, Evolutionary Algorithms and Applications. Over its lifetime it has published 145 papers, which have received 1,175 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Evolutionary Learning and Optimization 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Evolutionary Learning and Optimization style. No sign-up. |
| Publishes research in | Advanced Multi-Objective Optimization Algorithms Metaheuristic Optimization Algorithms Research Evolutionary Algorithms and Applications Reinforcement Learning in Robotics Machine Learning and Data Classification |
| ISSN | 2688-299X |
| Citation impact (2-yr) | 3.29 |
| h-index | 17 |
| i10-index | 30 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Bayesian Optimization (BO), the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years. In particular, much attention has been paid to improving its efficiency on problems with many parameters to optimize. This attention has trickled down to the workhorse of high-dimensional BO, high-dimensional Gaussian process regression,…
The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper Confidence Bound (UCB) always select solutions to be expensively evaluated on the Pareto front, but Probability of Improvement…
Hyperparameter optimization constitutes a large part of typical modern machine learning (ML) workflows. This arises from the fact that ML methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or…
Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, however, involves manual setup, execution, and analysis of the experiment on an individual basis, which is laborious and can be mitigated by a…
This article pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e., they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability…
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