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bio-inspired computing

Contributor(s): Matthew Haughn

Bio-inspired computing is a research method aimed at solving problems using computer models based on the principles of biology and the natural world. Commonly seen as a philosophical approach, bio-inspired computing is used in a number of related fields of study within computing, rather than a field of study itself. Bio-inspired computing is an extension of the related field of biomimicry.

Bio-inspired computing puts less focus on optimized, high-speed algorithms and more focus on tractability and dependability. Generally, the approach is ground-up, rather than taking a large foundation of knowledge and adding artificial intelligence to it. Bio-inspired computing often takes a small foundation of set rules and builds upon them by way of unsupervised deep learning in training.

In cases where a problem has no clear solution, a useful change of perspective can make for a new chance at solutions. A useful question to ask might be “does this problem have any parallels in nature?” If the answer is “yes” then that parallel can be studied to similarly find parallels in solutions. Humanity has found this philosophy to be useful in solving numerous problems.

Examples of bio-inspired computing can often be found in AI, especially in machine learning where the learning processes of biological organisms can be emulated. Applications of bio-inspired computing include:

  • Genetic algorithms
  • Neural networks
  • Biodegradability prediction
  • Cellular automata
  • Emergent systems
  • Artificial life
  • Artificial immune systems
  • Graphics rendering
  • Lindenmayer systems
  • Network communications and protocols
  • Membrane computers
  • Excitable media
  • Sensory networks
  • Learning classifier systems
  • Robot design
This was last updated in April 2018

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