Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.

1. Research Scholar
Introduction to Soft Computing
TAMEEM AHMAD
PRESENTED
BY:
Department of Computer
Engineering, ZHCET
Zakir husain College of Engineering
& Technology

2. About AMU
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3. Soft Computing
• Research and Scope

4. Soft Computing, What is it?
• The idea behind soft computing is to model
cognitive behavior of human mind.
• Soft computing is foundation of conceptual
intelligence in machines.
• Use inexact solution to computationally hard
tasks (such as solution for NP-Complete problems, for which there is no
known algorithm that can compute an exact solution in polynomial time)
• Unlike hard computing , Soft computing is
tolerant of imprecision, uncertainty, partial truth,
and approximation.

5. Soft Computing, What is it? (Cont…)
• The idea of softcomputing was initiated in 1981 when Lofti A.Zadeh
published his first paper on soft data analysis “what is
softcomputing”, softcomputing. Springer-Verlag Germany/ USA,
1997.
• Zedeh, define softcomputing into one multidisciplinary system as the
fusion of the fields of Fuzzy Logic, Neuro-computing, Evolutionary
computing and Probabilistic Computing.
• Lofti A. Zedah, 1992: “softcomputing is an emerging approach to
computing which parallel the remarkable ability of human mind to
reason and learn in the environment of uncertainly and imprecision”

6. Soft Computing, What is it? (Cont…)
• Unlike hard computing , Soft computing is
tolerant of
– imprecision,
– uncertainty,
– partial truth, and
– approximation

7. Hard Vs Soft Computing
∙ Hard computing
− Based on the concept of precise modelling (mathematical or
analytical) and analyzing to yield accurate results.
− Works well for simple problems, but is bound by the NP-
Complete set.
∙ Soft computing
− Aims to surmount NP-complete problems.
− Uses inexact methods to give useful but inexact answers to
intractable problems.
− Represents a significant paradigm shift in the aims of
computing - a shift which reflects the human mind.
− Tolerant to imprecision, uncertainty, partial truth, and
approximation.
− Well suited for real world problems where ideal models are not
available.

8. Hard Vs Soft Computing (Cont…)
Hard Computing Soft Computing
Conventional computing requires a precisely
stated analytical model.
Soft computing is tolerant of imprecision.
Often requires a lot of computation time. Can solve some real world problems in
reasonably less time.
Not suited for real world problems for which
ideal model is not present.
Suitable for real world problems.
It requires full truth Can work with partial truth
It is precise and accurate Imprecise.
High cost for solution Low cost for solution
Require programs to be written Can evolve its own programs
Deterministic Stochastic
Require exact input Can deal with ambiguous and noisy data
Produce precise answer Produce approximate answers

9. Possibility Vs Soft Computing
Possibility Soft Computing
Does not have enough information to solve
the problem
Does not have enough information about the
problem itself.

10. Constituents of Soft Computing
− Fuzzy Logic (FL)
− Evolutionary Computation (EC) - based on the origin of
the species
 Genetic Algorithm
 Swarm Intelligence
 Ant Colony Optimizations
− Neural Network (NN)
− Machine Learning (ML)

11. SC development history

12. References
• Zadeh L. A. Soft Computing and Fuzzy Logic. IEEE Software 11 (6):
48-58, 1998.
• Lofti A.Zadeh. what is softcomputing”, softcomputing. Springer-
Verlag Germany/ USA, 1997.
• Rajasekaran S., G. A Vijayalaksmi Pai. Neural Network, Fuzzy Logic,
and Genetic Algorithms, Prentice Hall, 2005.
• K. Naresh, Sinha, M. Gupta. Soft Computing and Intelligent Systems
– Theory and Applications, Academic Press, 2000.
• Fahreddine Karray. Soft Computing and Intelligent System Design –
Theory, Tools and Applications, Addison Weslay, 2004.
• Tettamanzi, Andrea, Tomassine. Soft Computing: Integrating
Evolutionary, Neural and Fuzzy Systems, Springer, 2001.
• J. S. R Jang, C. T. Sun. Neuro-Fuzzy and SoftComputing: A
Computational Approach to Learning and Machine Intelligance,
Prentice Hall, 1996.