Machine Learning and Evolutionary Computation: AI Theory and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 267
Special Issue Editor
Interests: data mining; evolutionary computation; bioinformatics; machine learning
Special Issue Information
Dear Colleagues,
This Special Issue is aimed at providing recent advances in the development and application of methods, strategies, and frameworks in both machine learning and evolutionary computation. The application of these fields to engineering, health, and in general, solving real-life problems is also vitally important. Theoretical aspects of artificial intelligence that provide results in the above fields as well as practical results are also welcome.
Machine learning can be approached as the systematic study of algorithms and systems, which improve their knowledge or performance based on experience. The building of machines able to learn from experience has proven to have a meaningful level of learning ability. Thus, the introduction of machine learning techniques in solving computer problems is of vital importance since there exist problems that cannot be solved through common techniques.
Evolutionary computing (EC) captured popular attention, mainly in the field of engineering, since it is based on the Darwinian principle of evolution: survival of the fittest. Evolutionary principles are abstracted by EC techniques into algorithms that can be used to find optimal solutions to a problem. Thus, EC provides an efficient, adaptive, and robust search in optimization processes that are normally applied to very large, complex, and multimodal search spaces.
This way, machine learning and evolutionary computation take advantage of advances in the fields of artificial intelligence and statistics. Both disciplines have been working on problems of pattern recognition, discovery, and classification.
In this Special Issue, potential topics include, but are not limited to:
- Machine learning.
- Evolutionary computation.
- Artificial life.
- Data mining.
- Case-based reasoning.
- Data clustering.
- Combinatorial optimization.
- Reinforcement learning.
- Deep learning.
Dr. José Antonio Castellanos Garzón
Guest Editor
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