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In computer science, soft computing is the use of inexact solutions to computationally hard tasks such as the solution of NP-complete problems, for which there is no known algorithm that can compute an exact solution in polynomial time. Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.
Soft computing (SC) solutions are unpredictable, uncertain and between 0 and 1. Soft Computing became a formal area of study in Computer Science in the early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. However, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.
There are main differences between soft computing and possibility. Possibility is used when we don't have enough information to solve a problem but soft computing is used when we don't have enough information about the problem itself. These kinds of problems originate in the human mind with all its doubts, subjectivity and emotions; an example can be determining a suitable temperature for a room to make people feel comfortable.
Components of soft computing include:
- Neural networks (NN)
- Support Vector Machines (SVM)
- Fuzzy logic (FL)
- Evolutionary computation (EC), including:
- Ideas about probability including:
- Chaos theory
Generally speaking, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis). Soft computing techniques are intended to complement each other.
Unlike hard computing schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. Another common contrast comes from the observation that inductive reasoning plays a larger role in soft computing than in hard computing.
Bioinformatics and Biomedicine
SC has attracted close attention of researchers and has also been applied successfully to solve problems in bioinformatics and biomedicine. Nevertheless, the amount of information from biological experiments and the applications involving large-scale high-throughput technologies is rapidly increasing nowadays. Therefore, the ability of being scalable across large-scale problems becomes an essential requirement for modern SC approaches.
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- X. S. Yang, Z. H. Cui, R. Xiao, A. Gandomi, M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, Elsevier, (2013).
- D. K. Chaturvedi,Soft Computing: Techniques and Its Applications in Electrical Engineering, Springer, (2008).
- Yudong, Zhang; Saeed, Balochian; Vishal, Bhatnagar (2014). "Emerging Trends in Soft Computing Models in Bioinformatics and Biomedicine". The Scientific World Journal 2014: 3. doi:10.1155/2014/683029.