What is Soft-Computing?

Soft-Computing is a collection of techniques spanning many fields that fall under various categories in Computational Intelligence. Soft-Computing has three main branches : Fuzzy Logic, Evolutionary Computation, and Neural Networks. A number of other Soft-Computing techniques do not fall neatly under any of these three branches. These would include Bayesian Networks, Support-Vector Machines, Neuro-Fuzzy Systems and most hybrid systems, wavelet theory, theory of fractals, chaos theory, to name a few.

Soft computing refers to a collection of computational techniques in computer science, machine learning and some engineering disciplines, which study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Soft Computing uses soft techniques contrasting it with classical artificial intelligence hard computing techniques. Hard computing is bound by a Computer Science concept called NP-Complete, which means, in layman's terms, that there is a direct connection between the size of a problem and the amount of resources needed to solve the problem (there are problems so large that it would take the lifetime of the Universe to solve them, even at super computing speeds). Soft computing aids to surmount NP-complete problems by using inexact methods to give useful but inexact answers to intractable problems.

There is no hard and fast rule that would classify any single technique under “soft-computing”. However, there are some characteristics of soft-computing techniques which, taken together, serve to sketch the boundaries of the field.

Soft-computing, as opposed to “hard computing”, is rarely prescriptive in its solution to a problem. Solutions are not programmed for each and every possible situation. Instead, the problem or task at hand is represented in such a way that the “state” of the system can somehow be measured and compared to some desired state. The quality of the system’s state is the basis for adapting the system’s parameters, which slowly converge towards the solution. This is the basic approach employed by genetic algorithms and neural networks.

Soft-computing is often robust under noisy input environments and has high tolerance for imprecision in the data on which it operates. Lotfi Zadeh, founder of Fuzzy Logic, says of Computing with Words (CW) : “Computing, in its usual sense, is centered on manipulation of numbers and symbols. In contrast, CW is a methodology in which objects of computation are words and propositions drawn from natural langauage … There are two major imperatives for computing with words. First computing with words is a necessity when the available information is too imprecise to justify the use of numbers; and second, when there is tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost and better rapport with reality”.

Soft Computing became a formal Computer Science area of study in the early 1990's.[1] Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and

similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive.

Components of soft computing include:

Neural networks (NN) Fuzzy systems (FS)

Evolutionary computation (EC), including:

o Evolutionary algorithms

o Harmony search

Swarm intelligence

Ideas about probability including:

o Bayesian network

o Chaos theory

Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, 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.