Computational Intelligence (CI) is an offshoot of artificial intelligence in which the emphasis is placed on heuristic algorithms such as fuzzy systems, neural networks and evolutionary computation. It is usually contrasted with ‘traditional’, ’symbolic’ or ‘good old fashioned artificial intelligence (GOFAI)’. The IEEE Computational Intelligence Society uses the tag-line ‘Mimicking Nature for Problem Solving’ to describe Computational Intelligence, although mimicking nature is not a necessary element.

In addition to the three main pillars of CI (fuzzy systems, neural networks and evolutionary computation), Computational Intelligence also encompasses elements of learning, adaptation, heuristic and meta-heuristic optimisation, as well as any hybrid methods which use a combination of one or more of these techniques. More recently, emerging areas such as artificial immune systems, swarm intelligence, chaotic systems, and others, have been added to the range of Computational Intelligence techniques. The term ‘Soft Computing’ is sometimes used almost interchangeably with Computational Intelligence.

Computational Intelligence techniques have been successfully employed in a wide range of application areas, including decision support, generic clustering and classification, consumer electronic devices, stock market and other time-series prediction, combinatorial optimisation, medical, biomedical and bioinformatics problems, and many, many others. Although CI techniques are often inspired by nature, or mimick nature in some way, CI applications are not restricted to solving problems from nature.