Dear Colleagues,

In recent years, evolutionary computation has been a proven approach for combinatorial optimisation. From the efficient modelling of several theoretical and applied problems to efficient incomplete search, evolutionary computation is at the forefront of AI-based optimisation. Moreover, the wide applicability of evolutionary computational methods, especially to real-world problems, demonstrates the need for the continued advancement of this field of research.

This Special Issue aims to bring together recent advances in evolutionary computation, with an emphasis on the application of evolutionary optimisation methods to real-world problems, the integration of evolutionary computation with methods from AI and operations research, and improving evolutionary computation via machine learning.

Areas of interest for this Special Issue include (but are not limited to):

• Theoretical and applied studies in evolutionary computation;
• Combining methods from operations research and evolutionary computation (metaheuristics);
• Enhancing evolutionary computational methods via machine learning;
• Applications to real-world and industrial problems;
• Large-scale evolutionary computation.

Dr. Dhananjay R. Thiruvady
Dr. Su Nguyen
Dr. Yuan Sun
Guest Editors

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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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• evolutionary algorithms
• genetic algorithms
• metaheuristics
• multi-objective optimisation
• scheduling
• logistics
• machine learning
• large-scale optimisation