Fusion of soft computing and hard computing techniques: a review of applications

Abstract

Soft computing (SC) is an emerging collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low total cost. It differs from conventional hard computing (HC) in the sense that, unlike hard computing, it uses intuition or subjectivity. Therefore, soft computing provides an attractive opportunity to represent ambiguity in human thinking with the real life uncertainty. Fuzzy logic (FL), neural networks (NN), and genetic algorithms (GA) are the core methodologies of soft computing. However, FL, NN, and GA should not be viewed as competing with each other but synergistic and complementary instead, as emphasized by Dr. Zadeh . Considering the available literature, it is easy to conclude that the fusion of individual soft computing methodologies has been advantageous in numerous applications. In this paper, we give a review of applications where the fusion of soft computing and hard computing has provided innovative solutions for demanding real-world problems. A representative list of references is provided with evaluative discussions and conclusions.