Soft Computing MCQs

In this article, we will discuss the most commonly asked multiple-choice questions related to Soft Computing.

The main purpose of writing this article is to target competitive exams and interviews. Generally, Soft Computing involves the basics of Fuzzy Logic, Neural Networks, and Genetic Algorithms. Here, we will try to cover all the frequently asked Soft Computing questions with the correct choice of answer among various options.

1) Which of the following is associated with fuzzy logic?

1. Crisp set logic
2. Many-valued logic
3. Two-valued logic
4. Binary set logic

2) The truth values of traditional set theory can be defined as _________ and that of fuzzy logic is termed as _________.

1. Either 0 or 1, either 0 or 1.
2. Between 0 & 1, either 0 or 1.
3. Either 0 or 1, between 0 & 1.
4. Between 0 & 1, between 0 & 1.

3) A Fuzzy logic is an extension to the Crisp set, which handles the Partial Truth.

1. True
2. False

4) How many types of random variables are there in Fuzzy logic?

1. 2
2. 4
3. 1
4. 3

5) Which of the following represents the values of set membership?

1. Degree of truth
2. Probabilities
3. Discrete set
4. Both a & b

6) The probability density function is represented by

1. Continuous variable
2. Discrete variable
3. Probability distributions for Continuous variables
4. Probability distributions

7) _________is used for probability theory sentences.

1. Logic
2. Extension of propositional logic
3. Conditional logic
4. None of the above

8) Which of the following fuzzy operators are utilized in fuzzy set theory?

1. AND
2. OR
3. NOT
4. EX-OR

9) What is the name of the operator in fuzzy set theory, which is found to be linguistic in nature?

1. Lingual Variable
2. Fuzz Variable
3. Hedges
4. None of the above

10) Where can we use the Bayes rule?

1. To increase the complexity.
2. To decrease the complexity.
3. To solve queries
4. To answer the probabilistic query

11) Which of the following is offered by the Bayesian network?

1. Partial description of the domain
2. A complete description of the domain
3. A complete description of the problem
4. None of the above

12) _________ represents the fuzzy logic

1. IF-THEN rules
2. IF-THEN-ELSE rules
3. Both a & b
4. None of the above

13) Uncertainty can be represented by _________

1. Entropy
2. Fuzzy logic
3. Probability
4. All of the above

14) Name the algorithms that acquire from complex environments to generalize, approximate and simplify solution logic.

1. Ecorithms
2. Fuzzy set
3. Fuzzy Relational DB
4. None of the above

15) Which of the following condition can directly influence a variable by all the others?

1. Fully connected
2. Local connected
3. Partially connected
4. None of the above

16) A perceptron can be defined as _________

1. A double layer auto-associative neural network
2. A neural network with feedback
3. An auto-associative neural network
4. A single layer feed-forward neural network with pre-processing

17) What is meant by an auto-associative neural network?

1. A neural network including feedback
2. A neural network containing no loops
3. A neural network having a single loop
4. A single layer feed-forward neural network containing feedback

18) Which of the following is correct?

I. In contrast to conventional computers, neural networks have much higher computational rates.

II. Neural networks learn by example.

III. Neural networks mimic the same way as that of the human brain

1. All of the above
2. (ii) and (iii) are true
3. (i), (ii) and (iii) are true
4. None of the above

19) Which of the following is correct for the neural network?

I. The training time is dependent on the size of the network

II. Neural networks can be simulated on the conventional computers

III. Artificial neurons are identical in operation to a biological one

1. All of the above
2. (ii) is true
3. (i) and (ii) are true
4. None of the above

20) What are the advantages of neural networks over conventional computers?

I. Neural networks learn from examples

II. They are more fault-tolerant

III. They are well suited for real-time operation due to their high computational rates

1. (i) and (ii) are correct
2. (i) and (iii) are correct
3. Only (i)
4. All of the above

21) Backpropagation can be defined as _________

1. It is another name given to the curvy function in the perceptron.
2. It is the transmission of errors back through the network to adjust the inputs.
3. It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
4. None of the above

22) Which of the following is not the promise of an artificial neural network?

1. It can survive the failure of some nodes
2. It can handle noise
3. It can explain the result
4. It has inherent parallelism

23) Having multiple perceptrons can solve the XOR problem satisfactorily because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

1. True - This works always, and these multiple perceptrons learn to classify even complex problems.
2. False - Perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
3. True - Perceptron can do this but are unable to learn to do it - they have to be explicitly hand-coded
4. False - Just having a single perceptron is enough

24) Based on _________ membership function can be used to solve empirical problems.

1. Knowledge
2. Learning
3. Examples
4. Experience

25) A 3-input neuron is trained to output a 0 when the input is 110 and a 1 when the input is 111. After generalization, the output will be 0, when and only when the input is:

1. 000 or 110 or 011 or 101
2. 000 or 010 or 110 or 100
3. 100 or 111 or 101 or 001
4. 010 or 100 or 110 or 101

Answer: b) 000 or 010 or 110 or 100

Explanation: Before generalization, the truth table is as follows:

Here, $ represents the don't know cases, and the output is random.

After generalization, the truth table will be:

26) A 4-input neuron has weights 1, 2, 3, and 4. The transfer function is linear, with the constant of proportionality being equal to 2. The inputs are 4, 10, 5, and 20, respectively. The output will be:

1. 76
2. 238
3. 123
4. 119

27) A neuro software can be defined as:

1. A powerful and easy neural network
2. A software that is used to analyze neurons
3. Software utilized by a neurosurgeon
4. A software aimed to assist experts in the real world

28) What is the name of the network, which includes backward links from the output to the inputs as well as the hidden layers?

1. Perceptron
2. Self-organizing maps
3. Multi-layered perceptron
4. Recurrent neural network

29) Which of the following is true for unsupervised learning?

1. Some specific output values are disclosed
2. Some specific output values aren't disclosed
3. No relevant inputs value is specified
4. Both inputs as well outputs are specified
5. Neither inputs nor outputs are given

30) What is involved in inductive learning?

1. Inconsistent Hypothesis
2. Consistent Hypothesis
3. Estimated Hypothesis
4. Irregular Hypothesis
5. Regular Hypothesis

31) Which of the following statement is correct?

1. Not all formal languages are context-free
2. All formal languages are context-free
3. All formal languages are like natural language
4. Natural languages are context-oriented free
5. Natural language is normal

32) Which of the following is incorrect?

1. The union and intersection of two context-free languages are context-free.
2. The reverse of context-free language is context-free, but its complement does not need to be.
3. Every regular language is context-free as it can be easily explained by regular grammar.
4. The intersection of a context-free language and a regular language is always context-free.
5. The intersection of two context-free languages is context-free.

33) Automated vehicle is an application of _________

1. Unsupervised learning
2. Supervised learning
3. Reinforcement learning
4. Active learning

34) _________ is not counted in different learning method.

1. Analogy
2. Memorization
3. Introduction
4. Deduction

35) Which of the following models are utilized for learning?

1. Neural networks
2. Decision trees
3. Propositional and FOL rules
4. All of the above

36) Which of the following is the correct example of active learning?

1. Dust Cleaning Machine
2. News Recommender System
3. Automated Vehicle
4. None of the above

37) Which of the following is termed exploratory learning?

1. Active learning
2. Supervised learning
3. Reinforcement learning
4. Unsupervised learning

38) _________ helps in modifying the performance element, assisting in making a better decision.

1. Learning element
2. Performance element
3. Changing element
4. None of the above

39) Which of the following is considered while determining the nature of the learning problem?

1. Problem
2. Feedback
3. Environment
4. All of the above

40) Which of the following is chosen among the multiple consistent hypotheses?

1. Ockham razor
2. Learning element
3. Razor
4. None of the above

41) Which of the following takes input as an object described by a set of attributes?

1. Graph
2. Decision graph
3. Tree
4. Decision tree

42) A neural network can answer

1. For Loop questions
2. What-if questions
3. If-The-Else Analysis questions
4. None of the above

43) Feature of ANN in which ANN creates its own organization of representation of information it receives during learning time is

1. Adaptive Learning
2. What-if analysis
3. Self-Organization
4. Supervised learning

44) In artificial neural network, interconnected processing elements are termed as _________

1. Weights
2. Nodes or neurons
3. Axon
4. Soma

45) Each connection link in ANN is linked with ________ that contains statics about the input signal.

1. Neurons
2. Activation function
3. Weights
4. Bias

46) Artificial neurons are capable enough to model original neurons networks similarly as they are found in the human brain

1. True
2. False

47) Name the input function received by neurons, which is also known as the neuron's internal state.

1. Weight
2. Bias
3. Activation or neuron's activity level
4. None of the above

48) What is the name of the process that represents modified elements of the DNA?

1. Selection
2. Mutation
3. Recombination
4. None of the above

49) Which of the following is the best representation of individual genes?

1. Coding
2. Conversion
3. Encoding
4. None of the above

50) What is the name of the operator that is functioned on the population?

1. Recombination
2. Reproduction
3. Mutation
4. None of the above

51) Name the selection method that is found to be less noisy.

1. Boltzmann solution
2. Remainder solution
3. Stochastic remainder solution
4. None of the above

52) In how many steps does a crossover operator proceed?

1. 2
2. 3
3. 4
4. 5

53) Which of the following best relate to reinforcement learning?

1. Error based learning
2. Backpropagation learning
3. Output-based learning
4. None of the above

54) ________ helps in converting a given bit pattern into another bit pattern by using logical bit-wise operation.

1. Masking
2. Segregation
3. Conversion
4. Inversion

55) The ________ causes all the bits in the first operand to shift to the left by the number of positions indicated by the second operand.

1. Shift right
2. Shift left
3. Shift operator
4. None of the above

56) Which of the following is not a specified method used for selecting the parents?

1. Tournament Selection
2. Steady-state
3. Elitism
4. Boltzmann selection

57) ________ deals with uncertainty problems with its own merits and demerits

1. Neuro-fuzzy
2. Neuro-genetic
3. Fuzzy-genetic
4. None

58) What does FAM stand for?

1. Fuzzy Association Memory
2. Fuzzy Associative Memory
3. Fuzzy Assist Memory
4. None of the above

59) Which of the following exhibits non-linear functions to any desired degree of accuracy?

1. Neuro-fuzzy
2. Neuro-genetic
3. Fuzzy-genetic
4. None of the above

60) Matrix crossover is also known as _________

1. One dimensional
2. Two dimensional
3. Three dimensional
4. None of the above