Soft Computing MCQ | Soft Computing viva questions

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Difference between soft computing and hard computing:

Aspect	Soft Computing	Hard Computing
Nature of Problems	Deals with uncertainties, vagueness, and imprecise information prevalent in real-world problems.	Deals with well-defined, deterministic problems with clear boundaries and constraints.
Inspiration	Inspired by human-like decision-making and problem-solving abilities.	Based on precise mathematical models and algorithms.
Components	Fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning.	Traditional algorithms, numerical methods, and logical reasoning.
Data Type	Suitable for handling imprecise and incomplete data.	Best suited for well-structured, precise data.
Applications	Real-world applications with uncertainty and ambiguity.	Scientific calculations, engineering applications with exact solutions.
Flexibility	More flexible and tolerant of imprecise inputs.	Rigid and requires accurate data and precise rules.

Introduction:

Q1. Soft computing is used to solve:

A. Liner issues
B. Non-linear issues
C. Both 1 and 2
D. None of the above

Ans. B

Soft computing is used to resolve non-linear, imprecise problems that normally require human intelligence, on the other hand hard computing is used to solve questions or problems that can be solved using perfect equations this is the main difference between soft and hard computing.

Q2. Depth first search algorithm starts searching from:

A. Any branch of the tree
B. Root of the tree
C. Top most branch of the tree
D. None of the above

Ans: B

As the name goes the depth search algorithm starts from root of a tree and goes to the maximum extent till the branch exists.

Q3. Using fuzzy logic what does we normally try to achieve?

A. Generate rules or learnings on it’s own
B. User AND-OR logic to encode human learning
C. Uses IF-THEN function to encode human learning
D. All of the above

Ans. C

Q4. Characteristic of AO* algorithm:

A. Few sub-problems are solved to get solution for main problem
B. There is only one main problem there are no sub-problems of it
C. IF-ELSE conditions are used to represent the solution
D. AND-OR graphs are used to represent the solution

Ans. D

Q5. Benefits of using recurrent neural network:

A. It does not remember it’s input or learnings from previous inputs
B. It remember it’s inputs as well as learnings from previous inputs
C. It does not require learnings from precious inputs
D. None of the above

Ans. B

As it remembers inputs from previous learnings as well that improves the accuracy of this model.

Q6. Which one of the below is a type of production system?

A. Batch
B. Continuous
C. Project
D. All of the above

Ans. D. All of the above

Q7. In supervised learning models are trained on:

A. Unlabelled data
B. Labelled data
C. Half labelled data – half unlabelled
D. None of the above

Ans. B. Labelled data

Supervised learning models are trained on given labeled data, using which it learns and creates rules based on which it then maps the unpredicted data to relevant buckets.

Q8. Out of hard computing and soft computing which one takes more time for computation?

A. Soft computing
B. Hard computing
C. Both takes almost same time
D. None of the above

Ans. A. Hard computing

As in soft computing intelligent computational steps are followed as a result computation time in it is less than w.r.t hard computing.

Q9. Neural networks or fuzzy logic are part of:

A. Soft computing
B. Hard computing
C. Both
D. None of the above

Ans. A. Soft computing

Q10. Which one of the two produces precise answers:

A. Soft computing
B. Hard computing
C. Both produces precise answers
D. None produces precise answers

Ans. B. Hard computing

Q11. In which of the two the program learns and evolves over time as the new data points comes into picture:

A. Soft computing
B. Hard computing
C. Both
D. None of the above

Ans. A. Soft computing

Q12. In soft computing the output doesn’t remains same when we train of same data, whereas in case of hard computing the output will always remains the same:

1. True
2. False

Ans. True

Q13. Hard computing allows parallel computation and soft computation follows sequential computation:

1. True
2. False

Ans. False

It’s opposite i.e. hard computing follows sequential computations, whereas soft computing allows parallel computation.

Q14. What are hybrid systems?

A. Combination of hard and soft computing methods
B. Combination of several hard computing methods
C. Combination of several soft computing methods
D. None of the above

Ans. C. Combination of several soft computing methods

Hybrid systems are combination of several soft computing methods for e.g. some hybrid systems examples are hybrid fuzzy logic systems, hybrid neural networks systems etc.

Q15. One human cell contains how many pairs of chromosomes?

A. 21
B. 22
C. 23
D. 24

Ans. C. 23

Q16. The entire combination of genes is called:

A. Phenotype
B. Genotype
C. Allele
D. None of the above

Ans. B. Genotype

Some other important questions on soft computing are:

1. What is soft computing, and how does it differ from hard computing?
2. Explain the main components of soft computing and their applications in problem-solving.
3. What is fuzzy logic, and how is it used in soft computing?
4. Describe the working principle of neural networks and their applications in pattern recognition and classification.
5. How do genetic algorithms mimic the process of natural selection, and what are their advantages in optimization problems?
6. Discuss the role of probabilistic reasoning in soft computing and its applications in uncertain environments.
7. Compare and contrast soft computing techniques (fuzzy logic, neural networks, genetic algorithms) with traditional algorithms in terms of problem-solving approaches.
8. How does soft computing handle imprecise and uncertain data in real-world applications?
9. Provide examples of real-world applications where soft computing has demonstrated superiority over hard computing approaches.
10. What are the limitations of soft computing, and in what scenarios would hard computing be more suitable?
11. Explain the concept of neuro-fuzzy systems and their advantages in combining fuzzy logic and neural networks.
12. How are adaptive neuro-fuzzy inference systems (ANFIS) used to model complex systems and make accurate predictions?
13. Discuss the challenges and future prospects of soft computing in the field of artificial intelligence and decision-making.
14. Describe the concept of swarm intelligence and its applications in optimization and collective problem-solving.
15. How does soft computing contribute to the development of intelligent systems and AI applications?

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