Abstract: Soft Computing is the science of reasoning, thinking and deductions. The idea behind soft computing is to model cognitive behavior of human mind. Soft computing is foundation of conceptual intelligence in machines. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth and approximation. 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. The main techniques in soft computing are evolutionary computing, artificial neural networks, fuzzy logic and Bayesian statistics. Each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex or inherently noisy to tackle with conventional mathematical methods. The applications of soft computing have proved two main advantages: first, it made solving non-linear problems, in which mathematical models are not available or possible and second, it introduced the human knowledge such as cognition, recognition, understanding, learning and others into the fields of computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various techniques of soft computing paradigm. Aim of this paper is to explore possibilities of applying soft computing techniques to the problems associated with various domains.
Keywords: Soft Computing, Artificial Neural Network, Fuzzy Logic, Evolutionary Computing, Machine Learning.