Explore topic-wise MCQs in Computer Science Engineering (CSE).

This section includes 124 Mcqs, each offering curated multiple-choice questions to sharpen your Computer Science Engineering (CSE) knowledge and support exam preparation. Choose a topic below to get started.

1.

What are normally the two best measurement units for an evolutionary algorithm?1. Number of evaluations2. Elapsed time3. CPU Time4. Number of generations

A. 1 and 2
B. 2 and 3
C. 3 and 4
D. 1 and 4
Answer» E.
Discussion
2.

There are also other operators, more linguistic in nature, called that can be applied to fuzzy set theory.

A. hedges
B. lingual variable
C. fuzz variable
D. none of the mentioned
Answer» B. lingual variable
Discussion
3.

Which of the following neural networks uses supervised learning? (A) Multilayer perceptron (B) Self organizing feature map (C) Hopfield network

A. (a) only
B. (b) only
C. (a) and (b) only
D. (a) and (c) only
Answer» B. (b) only
Discussion
4.

Given U = {1,2,3,4,5,6,7} A = {(3, 0.7), (5, 1), (6, 0.8)} then A will be: (where ~ complement)

A. {(4, 0.7), (2,1), (1,0.8)
B. {(4, 0.3.): (5, 0), (6
C. {(l, 1), (2, 1), (3, 0.3)
D. {(3, 0.3), (6.0.2)}
Answer» D. {(3, 0.3), (6.0.2)}
Discussion
5.

If A and B are two fuzzy sets with membership functions A(x) = {0.6, 0.5, 0.1, 0.7, 0.8} B(x) = {0.9, 0.2, 0.6, 0.8, 0.5}Then the value of (A B) (x) will be

A. {0.9, 0.5, 0.6, 0.8, 0.8
B. {0.6, 0.2, 0.1, 0.7,
C. {0.1, 0.5, 0.4, 0.2, 0.
D. {0.1, 0.5, 0.4, 0.2, 0.3}
Answer» D. {0.1, 0.5, 0.4, 0.2, 0.3}
Discussion
6.

Compute the value of adding the following two fuzzy integers:A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)}Where fuzzy addition is defined as A+B(z) = maxx+y=z (min( A(x), B(x))) Then, f(A+B) is equal to

A. {(0.5,12), (0.6,13), (1,
B. {(0.5,12), (0.6,13),
C. {(0.3,12), (0.5,13), (
D. {(0.3,12), (0.5,13), (0.6
Answer» E.
Discussion
7.

Choose the correct statement1. A fuzzy set is a crisp set but the reverse is not true2. If A,B and C are three fuzzy sets defined over the same universe of discourse such that A B and B C and A C3. Membership function defines the fuzziness in a fuzzy set irrespecive of the elements in the set, which are discrete or continuous

A. 1 only
B. 2 and 3
C. 1,2 and 3
D. none of these
Answer» C. 1,2 and 3
Discussion
8.

Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction A(x) = x / (x+2) Then the cut corresponding to = 0.5 will be

A. {0, 1, 2, 3, 4, 5, 6, 7, 8
B. {1, 2, 3, 4, 5, 6, 7,
C. {2, 3, 4, 5, 6, 7, 8, 9,
D. none of the above
Answer» D. none of the above
Discussion
9.

A fuzzy set whose membership function has at least one element x in the universe whose membership value is unity is called

A. sub normal fuzzy sets
B. normal fuzzy set
C. convex fuzzy set
D. concave fuzzy set
Answer» C. convex fuzzy set
Discussion
10.

Which selection strategy works with negative fitness value?

A. roulette wheel selection
B. stochastic universal sampling
C. tournament selection
D. rank selection
Answer» E.
Discussion
11.

EP applies which evolutionary operators?

A. variation through application of mutation operators
B. selection
C. both a and b
D. none of the mentioned
Answer» D. none of the mentioned
Discussion
12.

Which of these emphasize of development of behavioral models?

A. evolutionary programming
B. genetic programming
C. genetic algorithm
D. all the mentioned
Answer» B. genetic programming
Discussion
13.

Which crossover operators are used in evolutionary programming?

A. single point crossover
B. two point crossover
C. uniform crossover
D. evolutionary programming doesnot use crossover operators
Answer» E.
Discussion
14.

Which of the following operator is simplest selection operator?

A. random selection
B. proportional selection
C. tournament selection
D. none
Answer» B. proportional selection
Discussion
15.

Termination condition for EA

A. mazimally allowed cpu time is elapsed
B. total number of fitness evaluations reaches a given limit
C. population diveristy drops under a given threshold
D. all the mentioned
Answer» E.
Discussion
16.

(1+λ) ES

A. λ mutants can be generated from one parent
B. one mutant is generated
C. 2λ mutants can be generated
D. no mutants are generated
Answer» B. one mutant is generated
Discussion
17.

(1+1) ES

A. offspring becomes parent if offspring\s fitness is as good as parent of next generation
B. offspring become parent by default
C. offspring never becomes parent
D. none of the mentioned
Answer» B. offspring become parent by default
Discussion
18.

In Evolutionary Strategy,

A. individuals are represented by real- valued vector
B. individual solution is represented as a finite state machine
C. individuals are represented as binary string
D. none of the mentioned
Answer» B. individual solution is represented as a finite state machine
Discussion
19.

In Evolutionary programming,

A. individuals are represented by real- valued vector
B. individual solution is represented as a finite state machine
C. individuals are represented as binary string
D. none of the mentioned
Answer» C. individuals are represented as binary string
Discussion
20.

Evolutionary Strategies (ES)

A. (µ,λ): select survivors among parents and offspring
B. (µ+λ): select survivors among parents and offspring
C. (µ-λ): select survivors among offspring only
D. (µ:λ): select survivors among offspring only
Answer» C. (µ-λ): select survivors among offspring only
Discussion
21.

What are normally the two best measurement units for an evolutionary algorithm?1. Number of evaluations2. Elapsed time3. CPU Time4. Number of generations

A. 1 and 2
B. 2 and 3
C. 3 and 4
D. 1 and 4
Answer» E.
Discussion
22.

Step size in self-adaptive EP :

A. deviation in step sizes remain static
B. deviation in step sizes change over time using some deterministic function
C. deviation in step size change dynamically
D. size=1
Answer» D. size=1
Discussion
23.

Step size in dynamic EP :

A. deviation in step sizes remain static
B. deviation in step sizes change over time using some deterministic function
C. deviation in step size change dynamically
D. size=1
Answer» C. deviation in step size change dynamically
Discussion
24.

Step size in non-adaptive EP :

A. deviation in step sizes remain static
B. deviation in step sizes change over time using some deterministic function
C. deviation in step size change dynamically
D. size=1
Answer» B. deviation in step sizes change over time using some deterministic function
Discussion
25.

In Evolutionary strategy, recombination is

A. doesnot use recombination to produce offspring. it only uses mutation
B. uses recombination such as cross over to produce offspring
C. uses various recombination operators
D. none of the mentioned
Answer» C. uses various recombination operators
Discussion
26.

In Evolutionary programming, recombination is

A. doesnot use recombination to produce offspring. it only uses mutation
B. uses recombination such as cross over to produce offspring
C. uses various recombination operators
D. none of the mentioned
Answer» B. uses recombination such as cross over to produce offspring
Discussion
27.

In Evolutionary strategy, survival selection is

A. probabilistic selection (μ+μ) selection
B. (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
C. children replace the parent
D. all the mentioned
Answer» C. children replace the parent
Discussion
28.

In Evolutionary programming, survival selection is

A. probabilistic selection (μ+μ) selection
B. (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
C. children replace the parent
D. all the mentioned
Answer» B. (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
Discussion
29.

What Is Another Name For Fuzzy Inference Systems?

A. fuzzy expert system
B. fuzzy modelling
C. fuzzy logic controller
D. all of the above
Answer» E.
Discussion
30.

What Are The Two Types Of Fuzzy Inference Systems?

A. model-type and system-type
B. momfred-type and semigi-type
C. mamdani-type and sugeno-type
D. mihni-type and sujgani-type
Answer» D. mihni-type and sujgani-type
Discussion
31.

Mamdani's Fuzzy Inference Method Was Designed To Attempt What?

A. control any two combinations of any two products by synthesising a set of linguistic control rules obtained from experienced human operations.
B. control any two combinations of any two products by synthesising a set of linguistic control rules obtained from experienced human operations.
C. control a steam engine and a boiler combination by synthesising a set of linguistic control rules obtained from experienced human operations.
D. control a air craft and fuel level combination by synthesising a set of linguistic control rules obtained from experienced human operations.
Answer» D. control a air craft and fuel level combination by synthesising a set of linguistic control rules obtained from experienced human operations.
Discussion
32.

A fuzzy set wherein no membership function has its value equal to 1 is called

A. normal fuzzy set
B. subnormal fuzzy set.
C. convex fuzzy set
D. concave fuzzy set
Answer» C. convex fuzzy set
Discussion
33.

IF x is A and y is B then z=c (c is constant), is

A. rule in zero order fis
B. rule in first order fis
C. both a and b
D. neither a nor b
Answer» B. rule in first order fis
Discussion
34.

----- defines logic funtion of two prepositions

A. prepositions
B. lingustic hedges
C. truth tables
D. inference rules
Answer» D. inference rules
Discussion
35.

Multiple disjuctives antecedents is method of ----- in FLC

A. decomposition rule
B. formation of rule
C. truth tables
D. all of the above
Answer» B. formation of rule
Discussion
36.

Multiple conjuctives antecedents is method of ----- in FLC

A. decomposition rule
B. formation of rule
C. truth tables
D. all of the above
Answer» B. formation of rule
Discussion
37.

In fuzzy propositions, ---- gives an approximate idea of the number of elements of a subset fulfilling certain conditions

A. fuzzy predicate and predicate modifiers
B. fuzzy quantifiers
C. fuzzy qualifiers
D. all of the above
Answer» C. fuzzy qualifiers
Discussion
38.

A fuzzy set whose membership function has at least one element x in the universe whose membership valueis unity is called

A. sub normal fuzzy sets
B. normal fuzzy set
C. convex fuzzy set
D. concave fuzzy set
Answer» C. convex fuzzy set
Discussion
39.

Both fuzzy logic and artificial neural network are soft computing techniques because

A. both gives precise an
B. ann gives accura
C. in each, no precise
D. fuzzy gives exact resul
Answer» D. fuzzy gives exact resul
Discussion
40.

An equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is

A. fuzzy ≈ prediction
B. fuzzy ≈ forecastin
C. probability ≈ foreca
D. none of these
Answer» C. probability ≈ foreca
Discussion
41.

Choose the correct statement1. A fuzzy set is a crisp set but the reverse is not true2. If A,B and C are three fuzzy sets defined over the same universe of discourse such that A ≤ B and B ≤ C and A ≤ C3. Membership function defines the fuzziness in a fuzzy set irrespecive of the elements in the set, which are discrete or continuous

A. 1 only
B. 2 and 3
C. 1,2 and 3
D. none of these
Answer» C. 1,2 and 3
Discussion
42.

The fuzzy proposition "IF X is E then Y is F" is a

A. conditional unqualifi
B. unconditional unq
C. conditional qualifie
D. unconditional qualified
Answer» B. unconditional unq
Discussion
43.

Compute the value of adding the following two fuzzy integers:A = {(0.3,1), (0.6,2), (1,3), (0.7,4), (0.2,5)} B = {(0.5,11), (1,12), (0.5,13)}Where fuzzy addition is defined asμA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to

A. {(0.5,12), (0.6,13), (1,
B. {(0.5,12), (0.6,13),
C. {(0.3,12), (0.5,13), (
D. {(0.3,12), (0.5,13), (0.6
Answer» E.
Discussion
44.

Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership JunctionμA(x) = x / (x+2) Then the α cut corresponding to α = 0.5 will be

A. {0, 1, 2, 3, 4, 5, 6, 7, 8
B. {1, 2, 3, 4, 5, 6, 7,
C. {2, 3, 4, 5, 6, 7, 8, 9,
D. none of the above
Answer» D. none of the above
Discussion
45.

A U (B U C) =

A. (a ∩ b) ∩ (a ∩ c)
B. (a ∪ b ) ∪ c
C. (a ∪ b) ∩ (a ∪ c)
D. b ∩ a ∪ c
Answer» C. (a ∪ b) ∩ (a ∪ c)
Discussion
46.

Given U = {1,2,3,4,5,6,7} A = {(3, 0.7), (5, 1), (6, 0.8)}then A will be: (where ~ → complement)

A. {(4, 0.7), (2,1), (1,0.8)
B. {(4, 0.3.): (5, 0), (6
C. {(l, 1), (2, 1), (3, 0.3)
D. {(3, 0.3), (6.0.2)}
Answer» D. {(3, 0.3), (6.0.2)}
Discussion
47.

Any soft-computing methodology is characterised by

A. precise solution
B. control actions are unambiguous and accurate
C. control actions is formally defined
D. algorithm which can easily adapt with the change of dynamic environment
Answer» E.
Discussion
48.

What is ART in neural networks?

A. automatic resonance theory
B. artificial resonance theory
C. adaptive resonance theory
D. none of the mentioned
Answer» D. none of the mentioned
Discussion
49.

Operations in the neural networks can perform what kind of operations?

A. serial
B. parallel
C. serial or parallel
D. none of the mentioned
Answer» D. none of the mentioned
Discussion
50.

For what purpose Feedback neural networks are primarily used?

A. classification
B. feature mapping
C. pattern mapping
D. none of the mentioned
Answer» E.
Discussion