Symmetry in Evolutionary Algorithms

Dear Colleagues,

Symmetry is a fundamental and pervasive concept in evolutionary algorithms, influencing the design, efficiency, and performance of these optimization techniques. In the field of evolutionary algorithms, symmetry is a recurring theme, manifesting in various forms. One such instance is the symmetry of solution spaces, which allows for the discovery of equivalent optimal solutions. Another instance is found in the symmetry of operator designs (such as crossover and mutation) that ensure a balanced exploration and exploitation of the search space. For instance, symmetric fitness landscapes allow evolutionary algorithms to leverage patterns and regularities, reducing redundant computations and accelerating convergence. Symmetry is also a key factor in handling multimodal optimization problems. In such cases, symmetric subspaces often contain multiple optimal solutions. Recognizing such symmetries can enhance the algorithm's ability to find and preserve diverse solutions. Moreover, in real-world applications, from engineering design to complex system optimization, symmetry-aware evolutionary algorithms have demonstrated superior performance in solving problems with inherent symmetric structures, such as symmetric constraints or symmetric objective functions. In summary, symmetry is an indispensable element in advancing evolutionary algorithms, offering new perspectives for improving their robustness, efficiency, and applicability. This Special Issue aims to gather cutting-edge research on the integration of symmetry into evolutionary algorithms, exploring novel theories, methodologies, and practical applications. We particularly welcome studies that bridge the gap between symmetry theory and evolutionary algorithm design and demonstrate tangible impacts in solving real-world optimization challenges.

The focus of this Special Issue is to promote in-depth research on topics related to symmetry in evolutionary algorithms. Topics that are invited for submission include (but are not limited to) the following:

• Symmetric properties of solution spaces in evolutionary optimization;
• Theoretical analysis of evolutionary algorithm convergence;
• Novel operator designs for evolutionary optimization;
• Multi-objective evolutionary algorithms;
• Evolutionary algorithms for constrained optimization;
• Dynamic and adaptive evolutionary algorithms;
• Hybrid evolutionary algorithms with other computational paradigms;
• Evolutionary algorithms in engineering design and manufacturing;
• Evolutionary approaches to scheduling and resource allocation;
• Evolutionary algorithms for data mining and machine learning;
• Real-world applications of evolutionary algorithms in industry and science.

Prof. Dr. Xiangjuan Yao
Guest Editor

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.

• symmetric properties of solution spaces in evolutionary optimization
• theoretical analysis of evolutionary algorithm convergence
• novel operator designs for evolutionary optimization
• multi-objective evolutionary algorithms
• evolutionary algorithms for constrained optimization
• dynamic and adaptive evolutionary algorithms
• hybrid evolutionary algorithms with other computational paradigms
• evolutionary algorithms in engineering design and manufacturing
• evolutionary approaches to scheduling and resource allocation
• evolutionary algorithms for data mining and machine learning
• real-world applications of evolutionary algorithms in industry and science