Learning to Optimize, Optimizing to Learn: New Developments in Machine Learning and Optimization Integration
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 13
Special Issue Editors
Interests: operations research; machine learning
Special Issue Information
Dear Colleagues,
This Special Issue of Algorithms aims to showcase recent advances at the intersection of machine learning and optimization, with a particular focus on approaches that move beyond conventional use cases, and to present novel developments in intelligent decision-making systems.
We welcome contributions that present innovative methodologies, theoretical insights and algorithmic frameworks in which machine learning enhances optimization or optimization supports and informs learning. Topics of interest include, but are not limited to, the following:
- Machine learning for decision prediction;
- Optimization-informed machine learning;
- Learning-augmented algorithms and heuristics;
- Reinforcement learning and combinatorial optimization;
- Surrogate models for complex systems.
This Special Issue welcomes both theoretical contributions and application-oriented studies that demonstrate the potential and impact of integrating machine learning with optimization to address emerging challenges and opportunities at the forefront of the field.
Dr. Xiaochen Chou
Prof. Dr. Enza Messina
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- decision-making
- combinatorial optimization
- reinforcement learning
- surrogate models
- complex systems
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