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Computational intelligence (CI) is a set of Nature-inspired computational methodologies and approaches to address complex problems of the real world applications to which traditional (first principles, probabilistic, black-box, etc.) methodologies and approaches are ineffective or infeasible. It primarily includes Fuzzy logic systems, Neural Networks and Evolutionary Computation. In addition, CI also embraces techniques that stem from the above three or gravitate around one or more of them, such as Swarm intelligence and Artificial immune systems which can be seen as a part of Evolutionary Computation; Dempster-Shafer theory, Chaos theory and Multi-valued logic which can be seen as off-springs of Fuzzy Logic Systems, etc.

The characteristic of 'intelligence' is usually attributed to humans. More recently, many products and items also claim to be 'intelligent'. Intelligence is directly linked to the reasoning and decision making. Fuzzy logic was introduced in 1965 by Prof. L. A. Zadeh as a tool to formalise and represent the reasoning process and fuzzy logic systems which are based on fuzzy logic possess many characteristics attributed to intelligence. Fuzzy logic deals effectively with uncertainty that is common for human reasoning, perception and inference and contrary to some misconceptions has a very formal and strict mathematical backbone ('is quite deterministic in itself yet allowing uncertainties to be effectively represented and manipulated by it', so to speak). Neural networks, introduced in 1940s (further developed in 1980s) mimic the human brain and represent a computational mechanism based on a symplified mathematical model of the perceptrons (neurons) and signals that they process. Evolutionary computation, introduced by J. Holland in 1970s and more popular since 1990s mimics the population-based sexual evolution through reproduction of generations. It also mimics genetics in so called Genetic Algorithms. More recently, evolving intelligent systems has been proposed and developed that consider evolution of an individual and self-learning mimicking the way all living creatures, and especially humans learn from their experience and develop their own rules and model of the world around them; learn how to group (cluster), predict, classify and control objects, and processes. This is one of the latest building blocks of the CI which is still under intensive research and development.

References

Computational Intelligence: An Introduction by Andries Engelbrecht. Wiley & Sons. ISBN 0-470-84870-7
Computational Intelligence: A Logical Approach by David Poole, Alan Mackworth, Randy Goebel. Oxford University Press. ISBN 0-19-510270-3

External links

Journals

Software

Computational Intelligence Library (CILib)
OAT (Optimization Algorithm Toolkit): A set of standard computational intelligence optimization algorithms and problems in Java.
Evolving Intelligent Systems Toolbox(EST)

Organizations

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 '''Computational intelligence (CI)''' is a set of Nature-inspired computational methodologies and approaches to address complex problems of the real world applications to which traditional (first principles, probabilistic, black-box, etc.) methodologies and approaches are ineffective or infeasible. It primarily includes [[Fuzzy logic system]]s, [[Artificial Neural Network|Neural Networks]] and [[Evolutionary Computation]]. In addition, CI also embraces techniques that stem from the above three or gravitate around one or more of them, such as [[Swarm intelligence]] and [[Artificial immune system]]s which can be seen as a part of [[Evolutionary Computation]]; [[Dempster-Shafer theory]], [[Chaos theory]] and [[Multi-valued logic]] which can be seen as off-springs of [[Fuzzy Logic System]]s, etc.
-The characteristic of 'intelligence' is usually attributed to humans. More recently, many products and items also claim to be 'intelligent'. Intelligence is directly linked to the reasoning and decision making. [[Fuzzy logic]] was introduced in 1965 by Prof. L. A. Zadeh as a tool to formalise and represent the reasoning process and [[fuzzy logic system]]s which are based on [[fuzzy logic]] possess many characteristics attributed to intelligence. Fuzzy logic deals effectively with uncertainty that is common for human reasoning, perception and inference and contrary to some misconceptions has a very formal and strict mathematical backbone ('is quite deterministic in itself yet allowing uncertainties to be effectively represented and manipulated by it', so to speak). [[Neural networks]], introduced in 1940s (further developed in 1980s) mimic the human brain and represent a computational mechanism based on a mathematical representation of the perceptrons (neurons) and signals that they process. [[Evolutionary computation]], introduced by J. Holland in 1970s and more popular since 1990s mimics the population-based sexual evolution through reproduction of generations. It also mimics genetics in so called [[Genetic Algorithms]]. More recently, [[evolving intelligent system]]s has been proposed and developed that consider evolution of an individual and self-learning mimicking the way all living creatures, and especially humans learn from their experience and develop their own rules and model of the world around them; learn how to group (cluster), predict, classify and control objects, and processes. This is one of the latest building blocks of the CI which is still under intensive research and development.
+The characteristic of 'intelligence' is usually attributed to humans. More recently, many products and items also claim to be 'intelligent'. Intelligence is directly linked to the reasoning and decision making. [[Fuzzy logic]] was introduced in 1965 by Prof. L. A. Zadeh as a tool to formalise and represent the reasoning process and [[fuzzy logic system]]s which are based on [[fuzzy logic]] possess many characteristics attributed to intelligence. Fuzzy logic deals effectively with uncertainty that is common for human reasoning, perception and inference and contrary to some misconceptions has a very formal and strict mathematical backbone ('is quite deterministic in itself yet allowing uncertainties to be effectively represented and manipulated by it', so to speak). [[Neural networks]], introduced in 1940s (further developed in 1980s) mimic the human brain and represent a computational mechanism based on a symplified mathematical model of the perceptrons (neurons) and signals that they process. [[Evolutionary computation]], introduced by J. Holland in 1970s and more popular since 1990s mimics the population-based sexual evolution through reproduction of generations. It also mimics genetics in so called [[Genetic Algorithms]]. More recently, [[evolving intelligent system]]s has been proposed and developed that consider evolution of an individual and self-learning mimicking the way all living creatures, and especially humans learn from their experience and develop their own rules and model of the world around them; learn how to group (cluster), predict, classify and control objects, and processes. This is one of the latest building blocks of the CI which is still under intensive research and development.
 ==See also==