Soft computing

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Soft Computing refers to a collection of new computational techniques in computer science, artificial intelligence, machine learning, and many applied and engineering areas where one tries to study, model, and analyze very complex phenomena, those for which more precise scientific tools of the past were incapable of giving low cost, analytic, and complete solution. Scientific methods of previous centuries could model, and precisely analyse, merely, relatively simple systems of physics, classical Newtonian mechanics, and engineering. More complex cases such as systems related to biology and medicine, humanities, management sciences, and similar fields remained outside of the main territory of successful applications of precise mathematical, and analytical methods.

Among the most important areas of soft computing:

Neural networks (NN)
Fuzzy systems (FS)
Evolutionary computation, including: (EC)
- Evolutionary algorithms
- Swarm Intelligence
Ideas about probability including:
- Bayesian network
Chaos theory

Generally speaking, soft computing techniques resemble human reasoning more closely than traditional techniques, which are largely based on conventional logical systems, such as sentential logic and predicate logic, or rely heavily on the mathematical capabilities of a computer. Soft computing techniques are often used to complement each other in applications. It should be pointed out that simplicity and complexity of systems are relative, and certainly, most successful mathematical modelings of the past have also been challenging and very significant.

Unlike hard computing schemes, which strive for exactness and for 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.