The Knowledge Engineering Review (Impact Factor: 1.257, Scimago rank: Q2 in AI journals) is currently accepting submissions for a Special Issue on Evolutionary Machine Learning.
Evolutionary machine learning is a fast-growing sub-field of research within machine learning that leverages evolutionary methods to address machine learning problems, ranging from data mining to computer vision. This special issue aims to provide a venue for researchers interested in all aspects of evolutionary machine learning to disseminate high quality research, and to encourage collaboration between researchers involved in the field.

This special issue targets high-quality original research papers covering all aspects of evolutionary machine learning. A non-exhaustive list of the topics of interest is outlined in the full CfP at the end of this email. The submission of manuscripts that are extended versions of work previously published at a conference or workshop are also welcome, on the condition that there is a significant amount of new material in the submission (i.e. the submission should contain a minimum of 50% new material).
More details can be found on the Special Issue website:
https://www.cambridge.org/core/news/evolutionary-machine-learning

Papers accepted to the special issue will not be liable for any publication fees or article processing charges. The deadline for submissions to the special issue is the 01 September 2021.

Authors interested in submitting to the special issue can also optionally submit a preliminary paper to the NeuroEvolution at Work (NEWK) ACM Workshop (deadline 12 April 2021), which is run alongside the GECCO 2021 conference: https://www.newk2021.icar.cnr.it/

Please feel free to contact us if you have any questions about the special issue.

Yours sincerely,

Guest Editors
Karl Mason, National University of Ireland Galway, karl.mason@nuigalway.ie
Patrick Mannion, National University of Ireland Galway, patrick.mannion@nuigalway.ie

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Call for Papers - Special Issue of KER on Evolutionary Machine Learning

There has been an explosion of interest in machine learning research in recent years. Evolutionary machine learning is a fast-growing sub-field of research within machine learning that leverages evolutionary methods to address machine learning problems, ranging from data mining to computer vision. This special issue aims to provide a venue for researchers interested in all aspects of evolutionary machine learning to disseminate high quality research, and to encourage collaboration between researchers involved in the field.

This special issue targets high-quality original research papers covering all aspects of evolutionary machine learning. A non-exhaustive list of the topics of interest is outlined below. The submission of manuscripts that are extended versions of work previously published at a conference or workshop are also welcome, on the condition that there is a significant amount of new material in the submission (i.e. the submission should contain a minimum of 50% new material).

Guest Editors:
Karl Mason, National University of Ireland Galway, Ireland
Patrick Mannion, National University of Ireland Galway, Ireland

Topics of interest:
Evolutionary neural networks (neuroevolution)
Evolutionary reinforcement learning
Genetic programming
Evolutionary strategies
Artificial life
Evolutionary robotics
Image processing using neuroevolution
Supervised learning
Evolutionary game theory
Evolving multi-agent systems
Multi-objective neuroevolution
Deep neuroevolution
Applications of evolutionary machine learning to:
Healthcare
Cybersecurity
Energy systems
Environment
Finance
Other applications

Timeline:
Full paper submission deadline: 01 September 2021
Manuscript submissions will be considered for publication in the special issue on a continuous basis until the submission deadline. First decisions will be issued approximately 2 months after the initial manuscript submission. Submissions accepted for publication before the completion of the special issue will be available on the journal website shortly after acceptance.

Submission instructions:
All papers accepted to the special issue will have to meet the normal peer review and quality standards of the journal, and potential contributors are encouraged to browse through some recent issues of the journal to get a sense of the style and formatting. The journal homepage can be found at https://www.cambridge.org/core/journals/knowledge-engineering-review
To submit, authors should use the journal’s submission system at https://mc.manuscriptcentral.com/ker and select "Evolutionary Machine Learning" as the special collection type when entering the details of the manuscript.
Authors should follow the KER submission instructions when preparing their manuscript - these are available at https://www.cambridge.org/core/journals/knowledge-engineering-review/information/instructions-contributors