Soft Computing And Hard Computing

There are two types of computing methods namely, soft computing and hard computing.
The basic difference between the two is that the hard computing is a conventional
computing method which relies on the principles of certainty, accuracy, and inflexibility,
on the other hand, the soft computing is a modern methodology that relies on the
principles of approximation, flexibility, and uncertainty.
 Soft computing is an emerging approach to computing which parallel the
remarkable ability of the human mind to reason and learn in an environment of
uncertainty and imprecision.
 Soft computing is based on some biological inspired methodologies such as
genetics, evolution, ant’s behaviors, particles swarming, human nervous
systems, etc.
 Now, soft computing is the only solution when we don’t have any mathematical
modeling of problem solving (i.e., algorithm), need a solution to a complex problem
in real time, easy to adapt with changed scenario and can be implemented with
parallel computing.
 It has enormous applications in many application areas such as medical diagnosis,
computer vision, hand written character recondition, pattern recognition, machine
intelligence, weather forecasting, network optimization, VLSI design, etc.

What is Soft Computing?

Soft Computing is a modern computing model that evolved to resolve non-linear problems
that involve approximation, uncertainty, and imprecision. Thus, soft computing can be
associated with being liberal with inexactness, uncertainty, partial truth and
approximation. Soft computing mainly depends on the formal logic and probabilistic
reasoning.
The term "soft computing" was first coined by Dr Lotfi Zadeh. According to Dr Zadeh, soft
computing is a methodology that imitates the human brain to reason and learn in an
uncertain environment. Soft computing uses multivalued logics and thus has a
sophisticated nature. It is mainly used to perform parallel computations.

What is Hard Computing?

Hard Computing is a conventional approach used in computing and requires an accurately

stated analytical model. The term "hard computing" too was coined by Dr Lotfi Zadeh. In
fact, he coined this term before "soft computing". Hard computing depends on the binary
logic and crisp system.
Hard computing uses two-valued logic. Therefore, it has a deterministic nature. It produces
precise and accurate results. In hard computing, some definite control actions are
defined using a mathematical model or algorithm.
The major drawback of hard computing is that it is incapable in solving the real world
problems whose behavior is imprecise and their information being changing continuously.
Hard computing is mainly used to perform sequential computations.
Difference between Soft Computing and Hard Computing

The following table highlights the major differences between soft computing and hard
computing −

Soft Computing Hard Computing

It can be associated with being liberal with It requires a precise state analytic model.
inexactness, uncertainty, partial truth and
approximation.

It depends on formal logic and probabilistic It depends on binary logic and crisp
reasoning. system.

It consists of approximation and dis-positionality. Its features include precision and

categoricity.

It has a stochastic nature. It has a deterministic nature.

It generally works on ambiguous and noisy data. It works on exact data.

It can be used to perform parallel computations. It is used to perform sequential

computations.

It results in approximate results. It produces precise results.

It can come out with its own programs. The programs have to be written.

It incorporates randomness in its computations. It is settled in nature.

It uses multivalued logic. It uses the two-valued logic.

Conclusion

The most significant difference that you should note here is that hard computing is a
conventional approach used to solve a deterministic problem, whereas soft computing is a
modern approach used to solve uncertain and imprecise problems.

Characteristics of Soft Computing:

Some characteristics of Soft computing

o Soft computing provides an approximate but precise solution for real-life problems.
o The algorithms of soft computing are adaptive, so the current process is not affected
by any kind of change in the environment.
 o The concept of soft computing is based on learning from experimental data. It
means that soft computing does not require any mathematical model to solve the
problem.
o Soft computing helps users to solve real-world problems by providing approximate
results that conventional and analytical models cannot solve.
o It is based on Fuzzy logic, genetic algorithms, machine learning, ANN, and expert
systems.

Applications of Soft computing techniques:

What is Soft Computing?

Soft computing is an association of methods that works collaboratively for providing

flexible information processing capability for handling real-life ambiguous situations. The
computing exploits the tolerance of:

 Imprecision
 Uncertainty
 Approximate reasoning
 Partial truth

The above elements help the computing technique in order to achieve:

 Tractability
 Robustness
 Low-cost solutions

The main concept of soft computing is to devise a method of computation that will provide
an acceptable solution at a low cost for an imprecisely or precisely formulated problem.
Soft computation is the fusion of fields such as:

 Fuzzy logic
 Neuro computing
 Genetic computing
 Probabilistic computing

The two major advantages of soft computing are:

1. Firstly, it enables us to solve non-linear problems even if there is no mathematical model

available.
2. Secondly, it enacts the human mind and introduces features such as cognition, recognition,
understanding, learning and others.

Applications of soft computing

There are several applications of soft computing where it is used. Some of them are listed
below:

o It is widely used in gaming products like Poker and Checker.

o In kitchen appliances, such as Microwave and Rice cooker.
o In most used home appliances - Washing Machine, Heater, Refrigerator, and
AC as well.
o Apart from all these usages, it is also used in Robotics work (Emotional per Robot
form).
o Image processing and Data compression are also popular applications of soft
computing.
o Used for handwriting recognition.

As we already said that, soft computing provides the solution to real-time problems and
here you can see that. Besides these applications, there are many other applications of soft
computing.

Need of soft computing

Sometimes, conventional computing or analytical models does not provide a solution to

some real-world problems. In that case, we require other technique like soft computing to
obtain an approximate solution.

o Hard computing is used for solving mathematical problems that need a precise
answer. It fails to provide solutions for some real-life problems. Thereby for real-life
problems whose precise solution does not exist, soft computing helps.
o When conventional mathematical and analytical models fail, soft computing helps,
e.g., You can map even the human mind using soft computing.
o Analytical models can be used for solving mathematical problems and valid for ideal
cases. But the real-world problems do not have an ideal case; these exist in a non-
ideal environment.
o Soft computing is not only limited to theory; it also gives insights into real-life
problems.
o Like all the above reasons, Soft computing helps to map the human mind, which
cannot be possible with conventional mathematical and analytical models.

Elements of soft computing

Soft computing is viewed as a foundation component for an emerging field of conceptual

intelligence. Fuzzy Logic (FL), Machine Learning (ML), Neural Network (NN), Probabilistic
Reasoning (PR), and Evolutionary Computation (EC) are the supplements of soft
computing. Also, these are techniques used by soft computing to resolve any complex
problem.

Any problems can be resolved effectively using these components. Following are three
types of techniques used by soft computing:

o Fuzzy Logic
o Artificial Neural Network (ANN)
o Genetic Algorithms

Fuzzy Logic (FL)

Fuzzy logic is nothing but mathematical logic which tries to solve problems with an open
and imprecise spectrum of data. It makes it easy to obtain an array of precise conclusions.

Fuzzy logic is basically designed to achieve the best possible solution to complex problems
from all the available information and input data. Fuzzy logics are considered as the best
solution finders.

Neural Network (ANN)

Neural networks were developed in the 1950s, which helped soft computing to solve real-
world problems, which a computer cannot do itself. We all know that a human brain can
easily describe real-world conditions, but a computer cannot.

An artificial neural network (ANN) emulates a network of neurons that makes a human
brain (means a machine that can think like a human mind). Thereby the computer or a
machine can learn things so that they can take decisions like the human brain.

Artificial Neural Networks (ANN) are mutually connected with brain cells and created
using regular computing programming. It is like as the human neural system.
Genetic Algorithms (GA)

Genetic algorithm is almost based on nature and take all inspirations from it. There is no
genetic algorithm that is based on search-based algorithms, which find its roots in natural
selection and the concept of genetics.

In addition, a genetic algorithm is a subset of a large branch of computation.

Soft computing vs hard computing

Hard computing uses existing mathematical algorithms to solve certain problems. It

provides a precise and exact solution of the problem. Any numerical problem is an example
of hard computing.

On the other hand, soft computing is a different approach than hard computing. In soft
computing, we compute solutions to the existing complex problems. The result calculated
or provided by soft computing are also not precise. They are imprecise and fuzzy in nature.

Parameters Soft Computing Hard Computing

Computation Takes less computation time. Takes more computation time.

time

Dependency It depends on approximation It is mainly based on binary logic and

 and dispositional. numerical systems.

Computation Parallel computation Sequential computation

type

Result/Output Approximate result Exact and precise result

Example Neural Networks, such as Any numerical problem or traditional

Madaline, Adaline, Art methods of solving using personal
Networks. computers.

What Are Genetic Algorithms?

Over the past few years, there has been a terrific buzz around Artificial Intelligence (AI). Major
companies like Google, Apple, and Microsoft are actively working on the topic. In fact, AI is an
umbrella that covers lots of goals, approaches, tools, and applications. Genetic Algorithms (GA)
is just one of the tools for intelligent searching through many possible solutions.
GA is a metaheuristic search and optimization technique based on principles present in natural
evolution. It belongs to a larger class of evolutionary algorithms.

GA maintains a population of chromosomes—a set of potential solutions for the problem. The
idea is that “evolution” will find an optimal solution for the problem after a number of successive
generations—similar to natural selection.
GA mimics three evolutionary processes: selection, gene crossover, and mutation.

Similar to natural selection, the central concept of GA selection is fitness. The chromosomes that
are more fit have a better chance for survival. Fitness is a function that measures the quality of
the solution represented by the chromosome. In essence, each chromosome within the population
represents the input parameters. For example, if your problem contains two input parameters,
such as price and volume in trading, each chromosome will logically consist of two elements.
How the elements are encoded within the chromosome is a different topic.

During the selection, chromosomes form pairs of parents for breeding. Each child takes
characteristics from its parents. Basically, the child represents a recombination of characteristics
from its parents: Some of the characteristics are taken from one parent and some from another. In
addition to the recombination, some of the characteristics can mutate.
Because fitter chromosomes produce more children, each subsequent generation will have better
fitness. At some point, a generation will contain a chromosome that will represent a good enough
solution for our problem.

GA is powerful and broadly applicable for complex problems. There is a large class of
optimization problems that are quite hard to solve by conventional optimization techniques.
Genetic algorithms are efficient algorithms whose solution is approximately optimal. The well-
known applications include scheduling, transportation, routing, group technologies, layout
design, neural network training, and many others.

Genetic Algorithms are search algorithm based on mechanics of natural selection and natural genetics.
They combine survival of the fittest among string structures with a structured yet randomized
information exchange to form a search algorithm with some of the innovative flair of human touch. In
every new generation a new set of strings is created using bits and pieces of fittest of the old.

While randomized, genetic algorithms are no simple random walk. They efficiently exploit historical
information to speculate on new search points with expected improved performance. The Central
theme of research on genetic algorithms has been robustness the balance between efficiency and
efficacy necessary for survival in many different environments.

Genetic Algorithms are theoretically and empirically proven to provide robust search in complex spaces.
These algorithms are computationally simple and yet powerful in their search for improvement.

The key features of GAs are:

1. GAs work with a coding of the parameter set and not the parameter themselves.

2. GAs search from a population of points and not from a single point.

3. GAs use objective function information and not derivatives or other auxiliary knowledge.

4. GAs are probabilistic transition rules and not deterministic rules.