Difference between revisions 761119014 and 763691474 on enwiki

{{Distinguish|soft microprocessor}}
{{Expert subject|computer science|date=July 2009}}

In [[computer science]], '''soft computing''' (sometimes referred to as [[computational intelligence]], though CI does not have an agreed definition) is the use of inexact solutions to computationally hard tasks such as the solution of [[NP-complete]] problems, for which there is no known algorithm that can compute an exact solution in [[polynomial time]]. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and [[approximation]]. In effect, the role model for soft computing is the [[human]] [[mind]].

The principal constituents of Soft Computing (SC) are [[Fuzzy logic|Fuzzy Logic]] (FL), [[Evolutionary computation|Evolutionary Computation]] (EC), [[Machine learning|Machine Learning]] (ML) and [[Probabilistic logic|Probabilistic Reasoning]] (PR), with the latter subsuming [[Bayesian network|belief networks]], [[chaos theory]] and parts of learning theory.

==Introduction==
Soft computing (SC) solutions are unpredictable, uncertain and between 0 and 1. Soft Computing became a formal area of study in Computer Science in the early 1990s.<ref>Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing,"  Communication of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.</ref>  Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising i(contracted; show full)** [[Evolutionary algorithm]]s
*** [[Genetic algorithm]]s
*** [[Differential evolution]]
** [[Metaheuristic]] and [[Swarm Intelligence]]
*** [[Ant colony optimization]]
*** [[Particle swarm optimization]]
* Ideas about [[probability]] including:
** [[Bayesian network]]
⏎
* [[Chaos theory]]

Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal [[logical system]]s, such as [[sentential logic]] and [[predicate logic]], or rely heavily on computer-aided numerical analysis (as in [[finite element analysis]]). Soft computing techniques are intended to complement each other.

Unlike hard computing schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that [[inductive reasoning]] plays a larger role in soft computing than in hard computing.

==References==
{{reflist}}

==External links==
* [http://www.softcomputing.es EUROPEAN CENTRE FOR SOFT COMPUTING]
* [http://www.helsinki.fi/~niskanen/bisc.html BISC SIG IN PHILOSOPHY OF SOFT COMPUTING]
* http://www.soft-computing.de/def.html
* http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/1136/1/subudhi.pdf

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[[Category:Scientific modeling]]
[[Category:Artificial intelligence]]
[[Category:Semantic Web]]
[[Category:Soft computing]]