Soft Computing: A Survey

Soft Computing is the fusion of methodologies that were designed to model and enable solutions to real world problems, which are not modeled or too difficult to model, mathematically.

A grouping of methods that works synergistically with soft computing method provides one or another Real-life flexible information processing capacity to handle ambiguous situations. Its purpose is to use tolerance impurities, Uncertainty, and partial truth about the ability to detect logic, in order to achieve stability and cost principles with a view to resolving ambiguous or accurately estimated, preferably, the method of calculation leads to an acceptable solution, to devise problem formulation.

Soft Computing (SC) reflects the fact that the purpose of computing represents a significant shift in the human mind. Computers store and uncertain and lacking in categoricity widely remarkable ability to process information that is imprecise&uncertain.

This time, Soft Computing (SC) of the major components: are: Fuzzy Systems (FS), including Fuzzy Logic (FL); Evolutionary Computation (EC), including Genetic Algorithms (GA); Neural Networks (NN), including Neural Computing (NC); Machine Learning (ML); and Probabilistic Reasoning (PR). In this paper we focus on fuzzy methodologies and fuzzy systems, as they bring basic ideas to other SC methodologies.