Special Issue "Bio-Inspired Computational Techniques: Theory, Methods and Applications"
Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 1945
Interests: computational epidemiology; evolutionary computation; computational biology; artificial life; complex systems
Interests: machine learning; genetic programming; particle swarm optimization
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Special Issue in Entropy: Unconventional Methods for Particle Swarm Optimization II
Since the early days of Artificial Intelligence, nature has been seen as a great source of inspiration to provide solutions for complex problems. Under evolutionary pressure, aggregations of cells, tissues, organisms, and species are pushed to develop optimized skills for their survival, finding solutions that are often creative and unexpected. These emergent behaviors can be emulated to design computational techniques for tackling real-world problems. For over 70 years, bio-inspired algorithms represent a research area that takes inspiration from natural phenomena, in order to implement high-performance computing approaches and intelligent paradigms, with the aim of solving complex problems. Examples of bio-inspired computational techniques include, but are not limited to, artificial neural networks, evolutionary computation, swarm intelligence and ant colonies, just to mention a few. Bio-inspired computation not only results in a fruitful approach for solving real-world problems, but it also plays a fundamental role in artificial life, at the intersection between biology, mathematics, and computer science. Thanks to these approaches, models of natural phenomena can be built to interfere with their behavior.
The objective of this Special Issue is to collect original research articles as well as review articles that will draw the current framework of research in the area of bio-inspired algorithms and approaches to solve problems or to model phenomena in different domains.
Prof. Dr. Mario Dante Lucio Giacobini
Prof. Dr. Leonardo Vanneschi
Manuscript Submission Information
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- bio-inspired computing
- artificial neural networks
- evolutionary computation
- swarm intelligence
- theoretical foundations
- real-life applications