Top 60 most asked Soft Computing mcq questions | SCOA mcq

Q. GA stands for
A: genetic algorithm
B: genetic asssurance
C: genese alforithm
D: noneof these

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genetic algorithm

Q. LCS stands for
A: learning classes system
B: learning classifier systems
C: learned class system
D: none of these

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learning classifier systems

Q. GBML stands for
A: Genese based Machine learning
B: Genes based mobile learning
C: Genetic based machine learning
D: none of these

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Genetic based machine learning

Q. EV is dominantly used for solving ___.
A: optimization problems
B: NP problem
C: simple problems
D: none of these

optimization problems

Q. EV is considered as?
A: adaptive
B: complex
C: both a and b
D: none of these

both a and b

Q. Parameters that affect GA
A: initial population
B: selection process
C: fitness function
D: all of these

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all of these

Q. Fitness function should be
A: maximum
B: minimum
C: intermediate
D: noneof these

minimum

Q. Genetic algorithms are example of
A: heuristic
B: Evolutionary algorithm
C: ACO
D: PSO

Evolutionary algorithm

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

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offsprings

Q. ____ decides who becomes parents and how many children the parents have.
A: parent combination
B: Parent selection
C: Parent mutation
D: Parent replace

Parent selection

Q. Basic elements of EA are ?
A: Parent Selection methods
B: Survival Selection methods
C: both a and b
D: none of these

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both a and b

Q. 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

Hedges

Q. 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

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convex fuzzy set

Q. 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

(A) only

Q. 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

both associative & distributive

Q. 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

Self Organization

Q. 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

algorithm which can easily adapt with the change of dynamic environment

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

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none of the mentioned

Q. Operations in the neural networks can perform what kind of operations?
A: serial
B: parallel
C: serial or parallel
D: none of the mentioned

serial or parallel

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

adaptive resonance theory

Q. The values of the set membership is represented by ___________
A: Discrete Set
B: Degree of truth
C: Probabilities
D: Both Degree of truth & Probabilities

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Degree of truth

Q. 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. 0.2) }
C: {(l, 1), (2, 1), (3, 0.3), (4,1), (6,0.2), (7, 1)}
D: {(3, 0.3), (6.0.2)}

{(l, 1), (2, 1), (3, 0.3), (4,1), (6,0.2), (7, 1)}

Q. What are the following sequence of steps taken in designing a fuzzy logic machine ?
A: Fuzzification → Rule evaluation → Defuzzification
B: Fuzzification → Defuzzification → Rule evaluation
C: Rule evaluation → Fuzzification → Defuzzification
D: Rule evaluation → Defuzzification → Fuzzification

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Fuzzification → Rule evaluation → Defuzzification

Q. 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, 0.5}
C: {0.1, 0.5, 0.4, 0.2, 0.2}
D: {0.1, 0.5, 0.4, 0.2, 0.3}

{0.1, 0.5, 0.4, 0.2, 0.2}

Q. 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), (1,14), (0.7,15), (0.7,16), (1,17), (1,18)}
B: {(0.5,12), (0.6,13), (1,14), (1,15), (1,16), (1,17), (1,18)}
C: {(0.3,12), (0.5,13), (0.5,14), (1,15), (0.7,16), (0.5,17), (0.2,18)}
D: {(0.3,12), (0.5,13), (0.6,14), (1,15), (0.7,16), (0.5,17), (0.2,18)}

{(0.3,12), (0.5,13), (0.6,14), (1,15), (0.7,16), (0.5,17), (0.2,18)}

Q. A U (B U C) =
A: (A ∩ B) ∩ (A ∩ C)
B: (A ∪ B ) ∪ C
C: (A ∪ B) ∩ (A ∪ C)
D: B ∩ A ∪ C

(A ∪ B ) ∪ C

Q. 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, 9, 10}
B: {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
C: {2, 3, 4, 5, 6, 7, 8, 9, 10}
D: None of the above

{2, 3, 4, 5, 6, 7, 8, 9, 10}

Q. The fuzzy proposition “IF X is E then Y is F” is a
A: conditional unqualified proposition
B: unconditional unqualified proposition
C: conditional qualified proposition
D: unconditional qualified proposition

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conditional unqualified proposition

Q. 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

2 and 3

Q. An equivalence between Fuzzy vs Probability to that of Prediction vs Forecasting is
A: Fuzzy ≈ Prediction
B: Fuzzy ≈ Forecasting
C: Probability ≈ Forecasting
D: None of these

Fuzzy ≈ Forecasting

Q. Both fuzzy logic and artificial neural network are soft computing techniques because
A: Both gives precise and accurate result
B: ANN gives accurate result, but fuzzy logic does not
C: In each, no precise mathematical model of problem is acquired
D: Fuzzy gives exact result but ANN does not

In each, no precise mathematical model of problem is acquired

Q. 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

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normal fuzzy set

Q. —– defines logic funtion of two prepositions
A: prepositions
B: Lingustic hedges
C: truth tables
D: inference rules

truth tables

Q. 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

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Fuzzy quantifiers

Q. Multiple conjuctives antecedents is method of —– in FLC
A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above

decomposition rule

Q. Multiple disjuctives antecedents is method of —– in FLC
A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above

decomposition rule

Q. 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

rule in zero order FIS

Q. 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

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subnormal fuzzy set

Q. 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.

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

Q. What Are The Two Types Of Fuzzy Inference Systems?
A: Model-Type and SystemType
B: Momfred-type and Semigitype
C: Mamdani-type and Sugeno-type
D: Mihni-type and Sujganitype

Mamdani-type and Sugeno-type

Q. What Is Another Name For Fuzzy Inference Systems?
A: Fuzzy Expert system
B: Fuzzy Modelling
C: Fuzzy Logic Controller
D: All of the above

All of the above

Q. 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

Probabilistic selection (μ+μ) selection

Q. 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

(μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children

Q. In Evolutionary programming, recombination is
A: does not 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

does not use recombination to produce offspring. It only uses mutation

Q. In Evolutionary strategy, recombination is
A: does not 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

uses recombination such as cross over to produce offspring

Q. 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

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deviation in step sizes remain static

Q. 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

deviation in step sizes change over time using some deterministic function

Q. 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

deviation in step size change dynamically

Q. What are normally the two best measurement units for an evolutionary algorithm?
1. Number of evaluations
2. Elapsed time
3. CPU Time
4. Number of generations
A: 1 and 2
B: 2 and 3
C: 3 and 4
D: 1 and 4

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1 and 4

Q. 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

(µ+λ): Select survivors among parents and offspring

Q. 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

Individual solution is represented as a Finite State Machine

Q. 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

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Individuals are represented by real-valued vector

Q. (1+1) ES
A: offspring becomes parent if offspring’s fitness is as good as parent of next
B: generation offspring become parent by default
C: offspring never becomes parent
D: none of the mentioned

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offspring becomes parent if offspring’s fitness is as good as parent of next

Q. (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
a

λ mutants can be generated from one parent

Q. 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
d

All the mentioned

Q. Which of the following operator is simplest selection operator?
A: Random selection
B: Proportional selection
C: tournament selection
D: none

Random selection

Q. 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

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evolutionary programming doesnot use crossover operators

Q. (1+1) ES
A: Operates on population size of two
B: operates on populantion size of one
C: operates on populantion size of zero
D: operates on populantion size of λ

Operates on population size of two

Q. Which of these emphasize of development of behavioral models?
A: Evolutionary programming
B: Genetic programming
C: Genetic algorithm
D: All the mentioned

Evolutionary programming

Q. EP applies which evolutionary operators?
A: variation through application of mutation operators
B: selection
C: both a and b
D: none of the mentioned

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both a and b

Q. Which selection strategy works with negative fitness value?
A: Roulette wheel selection
B: Stochastic universal sampling
C: tournament selection
D: Rank selection

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Rank selection

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