Symmetry/Asymmetry in Evolutionary Computation and Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 688
Special Issue Editors
Interests: evolutionary computation; machine learning; clustering algorithm; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: evolutionary computation; machine learning; computer vision; path planning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Evolutionary computation (EC), a family of algorithms inspired by biological evolution for global optimization, has been developed for a few decades. Potential applications are wide-ranging and include problems from combinatorial optimization, multi-objective optimization, and numerical optimization in diverse domains, such as engineering, biology, medicine, robotics, etc. Machine learning (ML) is one of the most engaging research and application areas within computer science today. Its ability to find patterns, clusters, and hidden knowledge from data has allowed us to understand, model, and predict the behaviors of complex systems.
Nowadays, symmetry and asymmetry play important roles in various aspects of EC and ML. These concepts can be applied to algorithm design, problem representation, and solution optimization. Usually, the concept of a fitness landscape in EC assumes a certain level of symmetry where similar solutions have similar fitness values. This symmetry can help in navigating the search space more efficiently. Also, symmetry/asymmetry is critical in ML, particularly in the design of algorithms and the interpretation of data. For example, the structure of many neural networks assumes symmetry in the weights and biases.
This Special Issue aims to explore the current impact, advances, and applications of symmetry/asymmetry in EC and ML. For either EC or ML, understanding and exploiting symmetry can lead to more efficient algorithms, while recognizing and addressing asymmetry is crucial for the robustness and generalization of algorithms or models. Researchers and practitioners must be aware of these properties when designing algorithms and interpreting results. Moreover, it is interesting to combine ML and EMO ideas in a single coherent framework. EC is very helpful to solve complex ML problems, e.g., when the fitness function of the ML model has many local extremes that can trap sequential methods. Meanwhile, ML methods can be used to analyze EC population data to better understand the problem structure, to understand what makes the solutions optimal, and to create new and often superior solutions.
We invite submissions on recent advances in the theory and applications of evolutionary computation and machine learning that explore the role of symmetry and asymmetry.
Dr. Shuwei Zhu
Dr. Leilei Cao
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. 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
- machine learning
- swarm intelligence
- bio-inspired optimization
- pattern classification and clustering analysis
- symmetry in evolutionary single-objective and multi-objective optimization
- symmetry in regularization techniques
- symmetry in data augmentation
- symmetry in neural networks and deep learning
- symmetry and asymmetric similarity measures
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