Definition by Lotfi Zadeh
Soft computing differs from conventional (hard) computing in that,
unlike hard computing, it is tolerant of imprecision, uncertainty and
partial truth. In effect, the role model for soft computing is the human
mind. The guiding principle of soft computing is: Exploit the tolerance for
imprecision, uncertainty and partial truth to achieve tractability,
robustness and low solution cost.
At this juncture, the principal constituents of soft computing (SC)
are fuzzy logic (FL), neural network theory (NN) and probabilistic
reasoning (PR), with the latter subsuming belief networks, genetic
algorithms, chaos theory and parts of learning theory. What is important to
note is that SC is not a melange of FL, NN and PR. Rather, it is a
partnership in which each of the partners contributes a distinct
methodology for addressing problems in its domain. In this perspective, the
principal contributions of FL, NN and PR are complementary rather than
competitive.
Please see a
presentation on this topic for more details.