What is meant by soft computing?

Soft computing

 Soft computing is a branch of artificial intelligence that develops intelligent systems capable of solving complex, uncertain, and imprecise problems using methods inspired by nature, such as fuzzy systems, artificial neural networks, genetic algorithms, and swarm intelligence. Unlike "hard computing," which relies on exact logic and precise mathematical models, soft computing embraces uncertainty and approximation, making it suitable for real-world scenarios where data is incomplete or ambiguous. Common applications include medical diagnosis, pattern recognition, industrial process control, and home appliance intelligence.  

Soft computing is an approach where we compute solutions to the existing complex problems, where output results are imprecise or fuzzy in nature, one of the most important features of soft computing is it should be adaptive so that any change in environment does not affect the present process. The following are the characteristics of soft computing.

• It does not require any mathematical modeling for solving any given problem
• It gives different solutions when we solve a problem of one input from time to time
• Uses some biologically inspired methodologies such as genetics, evolution, particles swarming, the human nervous system, etc.
• Adaptive in nature.

There are three types of soft computing techniques which include the following.

1. Artificial Neural Network
2. Fuzzy Logic
3. Genetic algorithm

Artificial Neural Network

It is a connectionist modeling and parallel distributed network. There are of two types ANN (Artificial Neural Network) and BNN (Biological Neural Network). A neural network that processes a single element is known as a unit. The components of the unit are, input, weight, processing element, output. It is similar to our human neural system. The main advantage is that they solve the problems in parallel, artificial neural networks use electrical signals to communicate. But the main disadvantage is that they are not fault-tolerant that is if anyone of artificial neurons gets damaged it will not function anymore.

 An example of a handwritten character, where a character is written in Hindi by many people, they may write the same character but in a different form. As shown below, whichever way they write we can understand the character, because one already knows how the character looks like. This concept can be compared to our neural network system.

Fuzzy Logic

The fuzzy logic algorithm is used to solve the models which are based on logical reasoning like imprecise and vague. It was introduced by Latzi A. Zadeh in 1965. Fuzzy logic provides stipulated truth value with the closed interval [0,1]. Where 0 = false value, 1= true value.

An example of a robot that wants to move from one place to another within a short time where there are many obstacles on the way. Now the question arises is that how the robot can calculate its movement to reach the destination point, without colliding to any obstacle. These types of problems have uncertainty problem which can be solved using fuzzy logic.

Genetic Algorithm in Soft Computing

The genetic algorithm was introduced by Prof. John Holland in 1965. It is used to solve problems based on principles of natural selection, that come under evolutionary algorithm. They are usually used for optimization problems like maximization and minimization of objective functions, which are of two types of an ant colony and swarm particle. It follows biological processes like genetics and evolution.

The genetic algorithm can solve the problems which cannot be solved in real-time also known as the NP-Hard problem. The complicated problems which cannot be solved mathematically can be easily solved by applying the genetic algorithm. It is a heuristic search or randomized search method, which provides an initial set of solutions and generate a solution to the problem efficiently and effectively.

A simple way of understanding this algorithm is by considering the following example of a person who wants to invest some money in the bank, we know there are different banks available with different schemes and policies. Its individual interest how much amount to be invested in the bank, so that he can get maximum profit. There are certain criteria for the person that is, how he can invest and how can he get profited by investing in the bank. These criteria can be overcome by the “Evolutional Computing” algorithm like genetic computing.