When we talk about solving any problem, we often find ourselves coming to precise decisions. However, there are certain circumstances wherein we do not have enough information for exact calculations. In those cases, we make approximate calculations and come to probable solutions instead of one precise solution. This method of problem-solving is known as soft computing. Soft computing is, by definition, tolerant of uncertainty, imprecision, partial truth, and approximation. This allows researchers to try to solve problems that aren’t possible to be solved by traditional computational models. Soft computing is also termed as computational intelligence. Soft computing is used when the problem isn’t precisely specified, or there isn’t enough information available about the problem statement. Soft computing has numerous applications in real life.

Components of Soft-computing

Generally speaking, soft computing techniques are inspired and closer to the working of biological systems as compared to hard computing. Several computational methodologies come within the scope of soft computing; a few of them are listed below:

Machine Learning

Machine learning is a part of artificial intelligence that deals with the study of statistical models and algorithms that are fed to the computer systems for computational purposes.

Fuzzy Logic

Fuzzy logic, unlike traditional logic, can have multiple values ranging from 0 to 1. Fuzzy logic was developed with the intent that people make decisions based on non-numerical and imprecise information to make decisions.

Probabilistic Reasoning

Probabilistic logic combines logic and probability to deduce solutions for uncertain problems.

Evolutionary Computation

Evolutionary theory is a family of computational algorithms and methodologies that are inspired by biological evolution processes.

Advantages of Soft-computing

Most problems in real life do not offer numerical values for us to work with and find solutions to. Soft-computing solves just this. It aids in finding approximate solutions to problems that do not have definitive answers. Soft-computing, in its essence, is biologically inspired and gets its inspirations from various evolutionary processes. Due to this, the models of soft computing can be
  1. Linguistic
  2. Comprehensible
  3. Fast when computing
  4. Effective while solving real-world issues
The soft-computing models give a lot of flexibility for humans to define real-world problems in computational language. There are various advantages of soft-computing. Some of these are as summarized below:
  1. The methodologies are tolerant to imprecision and vagueness.
  2. It solves problems with an element of uncertainty as is found in real life.
  3. It can construct and perceive “linguistic variables.”
  4. It is capable of deriving approximate solutions to problems.
  5. It can deal with issues consisting of non-statistical data.
  6. It can form equations based on a range of overlapping values instead of those with hard boundaries.
Taking an example to understand the advantages better, when we say that the water is hot or lukewarm or cold, it means nothing to a computer with hard computing. However, with soft-computing methodologies, we can define ranges of temperatures for the water and create functions based on these ranges. With computational models such as fuzzy logic, the computer can determine the range which the temperature lies closest to and can conclude whether the water is hot or cold or lukewarm. Such calculations are impossible with hard computing.

Why opt for soft-computing when we have hard computing in place?

Soft-computing is different and more flexible as compared to hard computing. But what are the differences between hard computing and soft computing? Why do we need soft-computing when we already have hard computing models in order? Let’s go through a few differences between soft computing and hard computing:
  1. While soft computing is tolerant of imprecision and uncertainty, hard computing requires precise state analytical model.
  2. Soft computing uses approximation, while hard computing needs precision.
  3. Soft-computing algorithms are capable of improving themselves and are self-evolving. Hard computing algorithms need to be rewritten or tweaked from time-to-time to adapt to the changing needs of the ecosystem.
  4. Any system is allowed to have multiple values with soft computing. But with hard computing, a system can have just two values.
  5. Soft computing uses multi-valued logic, while hard computing uses binary logic. Multi-valued logic offers users ways of defining multiple states of a system. This capability gives the system flexibility to get as close to the real-world scenarios as possible.
So, we can safely say that soft computing is handy when it comes to solving tricky situations. It helps in getting a workable solution for ambiguous cases. And, with the complexity of situations rising every day, it can be a safe bet to assume that soft computing is going to be used as a part of mainstream computing. It is for a matter of the fact that soft computing can rise above its challenges and will be revolutionizing the way computing functions.