Entropy in Soft Computing and Machine Learning Algorithms
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 36558
Special Issue Editors
Interests: metaheuristic algorithms; bioinspired computation; image processing; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; bioinspired computation; multi-objective optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Soft computing and machine learning algorithms are used in different fields of science and technology. They are important tools designed to solve complex real-life problems under uncertainty.
Entropy is a powerful tool that has changed the analysis of information. The use of entropy has been extended in soft computing and machine learning methodologies, from measuring uncertainty to exploring and exploiting search spaces in optimization. Different kinds of entropy are used depending on what is required. Moreover, it is necessary to use soft computing and machine learning methods to provide accurate solutions to complex problems in the information era. Hybrid algorithms are also important; they merge skills from different approaches and make decisions based on different rules to accurately explore the possible solutions.
Since the fields of soft computing and machine algorithms are constantly growing, following all the different branches in which entropy is used is complicated. Considering the above, this Special Issue aims to present the latest advances in soft computing and machine learning algorithms that employ or solve problems where entropy is included. It also seeks to include literature reviews and surveys on related topics.
Dr. Diego OlivaDr. Salvador Miguel Hinojosa Cervantes
Guest Editors
Manuscript Submission Information
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Keywords
- Metaheuristics
- Bioinspired algorithms
- Cross entropy
- Shannon entropy
- Fuzzy entropy
- Machine learning
- Neural networks
- Swarm optimization
- Evolutionary computation
- Fuzzy logic
- Genetic algorithms
- Deep learning
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