A.ABRAHAM and C. GROSAN: SOFT COMPUTING FOR MODELLING AND SIMULATION

It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time varying environment, are important in tackling practical computing problems. In contrast with conventional artificial intelligence techniques which only deal with precision, certainty and rigor the guiding principle of soft computing is to exploit the tolerance for imprecision, uncertainty, low solution cost, robustness, partial truth to achieve tractability, and better rapport with reality [Zadeh, 1998]. Soft computing is a consortium of technologies involving approximate reasoning, function approximation, learning capabilities, and a methodology for systematic random search and optimization. These capabilities are combined in a complementary and synergetic fashion. Soft computing has evolved not only from a theoretical point of view but also with a large variety of realistic applications to consumer products and industrial systems. Applications of soft computing have provided the opportunity to integrate humanlike vagueness and real-life uncertainty into an otherwise hard computer programs.