Soft computing possesses a variety of special methodologies that work
synergistically and provides “intelligent” information processing capability,
which leads to knowledge discovery. Here the involvement of the methodologies
with learning is the key feature deemed to be essential for intelligence. In the
traditional computing the prime attention is on precision accuracy and certainty.
By contrast, in soft computing imprecision, uncertainty, approximate reasoning,
and partial truth are essential concepts in computation.
These features make the computation soft, which is similar to the neuronal
computation in human brain with remarkable ability so that it yields similar
outcomes but in much simpler form for decision-making in a pragmatic way.
Among the soft computing methodologies, the outstanding paradigms are neural
networks, fuzzy logic and genetic algorithms. In general, both neural networks
and fuzzy systems are dynamic, parallel processing systems that establish the
input output relationships or identify patterns as prime desiderata which are
searched for knowledge discovery in databases. By contrast, the GA paradigm
uses search methodology in a multidimensional space for some optimization
tasks. This search may be motivated by a functional relationship or pattern
identification, as is the case with neural network and fuzzy logic systems.