Symmetry in Evolutionary Computation and Reinforcement Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 381
Special Issue Editors
Interests: intelligent optimization; resource scheduling; task planning
Special Issues, Collections and Topics in MDPI journals
Interests: scheduling; evolutionary algorithm; reinforcement learning; computational intelligence
Interests: evolutionary computation; multi-objective optimization; reinforcement learning; satellite scheduling
Special Issue Information
Dear Colleagues,
Evolutionary computation and reinforcement learning are two distinct but related fields in machine learning and optimization. Symmetry is an important concept that has been studied in the fields of evolutionary computation and reinforcement learning. In evolutionary computation, symmetry can be exploited to improve the efficiency and performance of optimization algorithms. Symmetric representations of candidate solutions can reduce the search space and allow for more effective exploration. Researchers have investigated ways to incorporate symmetry into genetic algorithms, evolution strategies, and other evolutionary techniques. In reinforcement learning, symmetry can be leveraged to generalize learning across similar states or actions. If an agent encounters a state that is symmetric to a previously visited state, it can apply the same learned policy or value function.
The interplay between evolutionary computation and reinforcement learning, combined with the consideration of symmetry, has led to advancements in various applications, including robotics, cryptography, and optimization.
Prof. Dr. Lining Xing
Dr. Yanjie Song
Dr. Junwei Ou
Dr. Jian Wu
Guest Editors
Yue Zhang
Guest Editor Assistant
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. Symmetry 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 2400 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
- evolutionary computation
- reinforcement learning
- optimization robotics
- cryptography
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
