Biologically-inspired computing (also bio-inspired computing) is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers.
Some areas of study encompassed under the canon of biologically-inspired computing, and their biological inspirations:
- genetic algorithms ↔ evolution
- cellular automata ↔ life
- emergent systems ↔ ants, termites, bees, etc
- neural networks ↔ the brain
- artificial life ↔ life
- lindenmayer systems ↔ plant structures
Natural evolution is a good analogy to this method–the rules of evolution (selection, recombination/reproduction, and mutation) are in principle simple rules, yet over thousands of years have produced remarkably complex organisms. A similar technique is used in genetic algorithms.
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