Before grasping more about Soft Computing, let us know what Hard computing is. Hard-computing is a process where computers solve problems using mathematical algorithms and yield a precise output value. Here, the control action is a mathematical model, and it is unambiguous. An elementary example of Hard computing is solving a numerical problem. The peculiarities of Hard computing are beneficial. Then why do we need and use Soft Computing?

Soft computing is a type of computing model developed to resolve the non-linear issues that include imprecise, vague, precise, and approximate solutions of a muddle. The term Soft Computing was coined by Lotfi A.Zadeh in the 1980s. It is an emerging strategy that deals with approximation models and gives solutions to real-world problems. The Striking fact is that it can map a human mind with another human mind.

Soft computing aids the users by solving real-world complications by rendering approximate but precise solutions. The algorithms used in Soft Computing are flexible and adaptive. The hypothesis of Soft Computing relies on learning from the experimental data. It doesn't require any mathematical models(used in Hard computing) to solve real-world problems. The concept of Soft Computing utterly depends upon the concepts such as Fuzzy Logic, Artificial Neural Networks, genetic algorithms, and expert systems.

Let us look into an illustration. Consider the two strings String 1 = "lmn" and String 2 = "lmo". The problem is to check whether these two strings are identical. Everybody can easily answer that they are not. It doesn't require any specific algorithms for solving this. But it is 75% identical according to Soft Computing. Soft computing does not provide an accurate solution but a precise one. Now one can understand the discrepancy between hard computing and soft computing.

A Human Brain can recognize, solve, and find solutions to the real-world problems around our environment, but a computer or a machine can't do these things. But, this drawback is achieved through the invention of Artificial Neural Networks through which a machine can think like a human mind. It can analyze the problems, make decisions, and bring out a comprehensive solution. Artificial Neural Networks are a mathematical model implemented neurally and are associated with the neurons(brain cells) through regular computing programming.

A computer with ANN works synchronously and computes the problems within seconds. The fundamental intention of ANN is to perform numerous computational tasks at a faster rate than traditional systems. Soft computing in association with Artificial Neural Network performs pattern matching, approximation, vector quantization, data clustering, etc., These advanced features cannot be accessed in traditional systems through Hard Computing. ANN has an immense scope in the field of Data Mining, Medical research, automobile and machinery, animal behavior, etc.,

The term Fuzzy means vague or indistinct. In general, a statement has two solutions, either True or False. But according to Fuzzy Logic, the statement has multiple answers. It provides more flexibility to find the appropriate solution for a problem. This logic helps businessmen to make decisions accurately. The answers for 'Have you completed the task?' will be either Yes or No, but when fuzzy logic is applied, there will be more solutions like, yes, going to complete, not yet started, etc., This logic is related to Soft Computing as it also provides an approximate and precise key to the problem. Soft Computing with the Fuzzy Logic approaches on the concept of how a computer would make decisions much faster.

Genetic algorithms are adaptive computational procedures executed iteratively on a set of coded solutions, called population, with three basic operators: selection/reproduction, crossover, and mutation. A genetic algorithm is a subset of a large branch of computation. They are usually used to generate high-quality solutions for optimization problems.

Soft Computing is a new thought for advanced information processing. The objective is to realize a new approach for analyzing and creating flexible information processing of humans such as sensing, understanding, learning, recognizing, and thinking. This objective has been implemented successfully, and scientists are still working to improvise it more.