120+ Computer Engineering Soft Computing Solved MCQs

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .

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51.	Evolution Strategies is developed with
A.	selection
B.	mutation
C.	a population of size
D.	all of these
Answer» D. all of these

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52.	Evolution Strategies typically uses
A.	real-valued vector re
B.	vector representa
C.	time based represe
D.	none of these
Answer» A. real-valued vector re

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53.	in ES survival is
A.	indeterministic
B.	deterministic
C.	both a and b
D.	none of these
Answer» D. none of these

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54.	What is the first step in Evolutionary algorithm
A.	termination
B.	selection
C.	recombination
D.	initialization
Answer» D. initialization

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55.	Elements of ES are/is
A.	parent population siz
B.	survival populatio
C.	both a and b
D.	none of these
Answer» C. both a and b

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56.	What are different types of crossover
A.	discrete and interme
B.	discrete and conti
C.	continuous and inte
D.	none of these
Answer» A. discrete and interme

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57.	Determining the duration of the simulation occurs before the model is validated and te
A.	true
B.	false
Answer» B. false

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58.	cannot easily be transferred from one problem domain to another
A.	optimal solution
B.	analytical solution
C.	simulation solutuon
D.	none of these
Answer» C. simulation solutuon

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59.	Discrete events and agent-based models are usuallly used for .
A.	middle or low level o
B.	high level of abstr
C.	very high level of ab
D.	none of these
Answer» A. middle or low level o

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60.	doesnot usually allow decision makers to see how a solution to a en
A.	simulation ,complex
B.	simulation,easy p
C.	genetics,complex p
D.	genetics,easy problem
Answer» A. simulation ,complex

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61.	EC stands for?
A.	evolutionary comput
B.	evolutionary com
C.	electronic computa
D.	noneof these
Answer» A. evolutionary comput

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62.	GA stands for
A.	genetic algorithm
B.	genetic asssuranc
C.	genese alforithm
D.	noneof these
Answer» A. genetic algorithm

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63.	LCS stands for
A.	learning classes syste
B.	learning classifier
C.	learned class syste
D.	mnoneof these
Answer» B. learning classifier

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64.	GBML stands for
A.	genese based machi
B.	genes based mob
C.	genetic bsed machi
D.	noneof these
Answer» C. genetic bsed machi

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65.	EV is dominantly used for solving .
A.	optimization proble
B.	mnp problem
C.	simple problems
D.	noneof these
Answer» A. optimization proble

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66.	EV is considered as?
A.	adaptive
B.	complex
C.	both a and b
D.	noneof these
Answer» C. both a and b

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67.	Parameters that affect GA
A.	initial population
B.	selection process
C.	fitness function
D.	all of these
Answer» D. all of these

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68.	Fitness function should be
A.	maximum
B.	minimum
C.	intermediate
D.	noneof these
Answer» B. minimum

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69.	Applying recombination and mutation leads to a set of new candidates, called as ?
A.	sub parents
B.	parents
C.	offsprings
D.	grand child
Answer» C. offsprings

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70.	decides who becomes parents and how many children the parents have.
A.	parent combination
B.	parent selection
C.	parent mutation
D.	parent replace
Answer» B. parent selection

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71.	Basic elements of EA are ?
A.	parent selection methods
B.	survival selection methods
C.	both a and b
D.	noneof these
Answer» C. both a and b

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72.	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» A. hedges

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73.	A fuzzy set has a membership function whose membership values are strictly monotonically increasing or strictly monotonically decreasing or strictly monotonically increasing than strictly monotonically decreasing with increasing values for elements in the universe
A.	convex fuzzy set
B.	concave fuzzy set
C.	non concave fuzzy set
D.	non convex fuzzy set
Answer» A. convex fuzzy set

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74.	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» A. (a) only

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75.	What is the feature of ANNs due to which they can deal with noisy, fuzzy, inconsistent data?
A.	associative nature of networks
B.	distributive nature of networks
C.	both associative & distributive
D.	none of the mentioned
Answer» C. both associative & distributive

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76.	Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is
A.	adaptive learning
B.	self organization
C.	what-if analysis
D.	supervised learning
Answer» B. self organization

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77.	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» D. algorithm which can easily adapt with the change of dynamic environment

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78.	For what purpose Feedback neural networks are primarily used?
A.	classification
B.	feature mapping
C.	pattern mapping
D.	none of the mentioned
Answer» D. none of the mentioned

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79.	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» C. serial or parallel

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80.	What is ART in neural networks?
A.	automatic resonance theory
B.	artificial resonance theory
C.	adaptive resonance theory
D.	none of the mentioned
Answer» C. adaptive resonance theory

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81.	The values of the set membership is represented by
A.	discrete set
B.	degree of truth
C.	probabilities
D.	both degree of truth & probabilities
Answer» B. degree of truth

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82.	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» C. {(l, 1), (2, 1), (3, 0.3)

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83.	If A and B are two fuzzy sets with membership functions μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8} μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5} Then the value of μ(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» C. {0.1, 0.5, 0.4, 0.2, 0.

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84.	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 μA+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» D. {(0.3,12), (0.5,13), (0.6

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85.	A U (B U C) =
A.	(a ∩ b) ∩ (a ∩ c)
B.	(a ∪ b ) ∪ c
C.	(a ∪ b) ∩ (a ∪ c)
D.	b ∩ a ∪ c
Answer» B. (a ∪ b ) ∪ c

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86.	Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction μA(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» C. {2, 3, 4, 5, 6, 7, 8, 9,

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87.	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» A. conditional unqualifi

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88.	Choose the correct statement 1. A fuzzy set is a crisp set but the reverse is not true 2. 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 ≤ C 3. 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» B. 2 and 3

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89.	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» B. fuzzy ≈ forecastin

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90.	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» C. in each, no precise

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91.	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» B. normal fuzzy set

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92.	----- defines logic funtion of two prepositions
A.	prepositions
B.	lingustic hedges
C.	truth tables
D.	inference rules
Answer» C. truth tables

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93.	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» B. fuzzy quantifiers

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94.	Multiple conjuctives antecedents is method of ----- in FLC
A.	decomposition rule
B.	formation of rule
C.	truth tables
D.	all of the above
Answer» A. decomposition rule

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95.	Multiple disjuctives antecedents is method of ----- in FLC
A.	decomposition rule
B.	formation of rule
C.	truth tables
D.	all of the above
Answer» A. decomposition rule

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96.	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» A. rule in zero order fis

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97.	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» B. subnormal fuzzy set.

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98.	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» C. control a steam engine and a boiler combination by synthesising a set of linguistic control rules obtained from experienced human operations.

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99.	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» C. mamdani-type and sugeno-type

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100.	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» D. all of the above

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120+ Computer Engineering Soft Computing Solved MCQs

Evolution Strategies is developed with

Evolution Strategies typically uses

in ES survival is

What is the first step in Evolutionary algorithm

Elements of ES are/is

What are different types of crossover

Determining the duration of the simulation occurs before the model is validated and te

cannot easily be transferred from one problem domain to another

Discrete events and agent-based models are usuallly used for .

doesnot usually allow decision makers to see how a solution to a en

EC stands for?

GA stands for

LCS stands for

GBML stands for

EV is dominantly used for solving .

EV is considered as?

Parameters that affect GA

Fitness function should be

Applying recombination and mutation leads to a set of new candidates, called as ?

decides who becomes parents and how many children the parents have.

Basic elements of EA are ?

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

A fuzzy set has a membership function whose membership values are strictly monotonically increasing or strictly monotonically decreasing or strictly monotonically increasing than strictly monotonically decreasing with increasing values for elements in the universe

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

What is the feature of ANNs due to which they can deal with noisy, fuzzy, inconsistent data?

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

Any soft-computing methodology is characterised by

For what purpose Feedback neural networks are primarily used?

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

What is ART in neural networks?

The values of the set membership is represented by

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

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

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 μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to

A U (B U C) =

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

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

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

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

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

----- defines logic funtion of two prepositions

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

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

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

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

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

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

What Are The Two Types Of Fuzzy Inference Systems?

What Is Another Name For Fuzzy Inference Systems?

There are also other operators, more linguistic in nature, called

that can be applied to fuzzy set theory.

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

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

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
μA+B(z) = maxx+y=z (min(μA(x), μB(x))) Then, f(A+B) is equal to