# Most Asked Soft Computing and Optimisations Algorithms MCQs For Computer Engineering

#### 1. GA stands for

A: genetic algorithm
B: genetic assurance
C: genese algorithm
D: none of these

#### 2. LCS stands for

A: learning classes system
B: learning classifier systems
C: learned class system
D: none of these

#### 3. GBML stands for

A: Genese based Machine learning
B: Genes based mobile learning
C: Genetic based machine learning
D: none of these

#### 4. EV is dominantly used for solving ___.

A: optimization problems
B: NP problem
C: simple problems
D: none of these

B: complex
C: both a and b
D: none of these

#### 6. Parameters that affect GA

A: initial population
B: selection process
C: fitness function
D: all of these

A: maximum
B: minimum
C: intermediate
D: none of these

#### 8. Genetic algorithms are example of

A: heuristic
B: Evolutionary algorithm
C: ACO
D: PSO

A: sub parents
B: parents
C: offsprings
D: grandchild

#### 10. ____ decides who becomes parents and how many children the parents have.

A: parent combination
B: Parent selection
C: Parent mutation
D: Parent replace

#### 11. Basic elements of EA are?

A: Parent Selection methods
B: Survival Selection methods
C: both a and b
D: none of these

#### 12. 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

#### 13. 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

#### 14. 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

#### 15. 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

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

B: Self Organization
C: What-If Analysis
D: Supervised Learning

#### 17. Any soft-computing methodology is characterized by

A: Precise solution
B: control actions are unambiguous and accurate
C: control actions is formally defined
D: an algorithm which can easily adapt with the change of dynamic environment

Answer: an algorithm which can easily adapt with the change of dynamic environment

#### 18. For what purpose Feedback neural networks are primarily used?

A: classification
B: feature mapping
C: pattern mapping
D: none of the mentioned

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

A: serial
B: parallel
C: serial or parallel
D: none of the mentioned

#### 20. What is ART in neural networks?

A: automatic resonance theory
B: artificial resonance theory
D: none of the mentioned

#### 21. The values of the set membership is represented by ___________

A: Discrete Set
B: Degree of truth
C: Probabilities
D: Both Degree of truth & Probabilities

#### 22. 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)}

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

#### 23. 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

Answer: Fuzzification → Rule evaluation → Defuzzification

#### 24. 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}

Answer: {0.1, 0.5, 0.4, 0.2, 0.2}

#### 25. 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)}

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

#### 26. A U (B U C) =

A: (A ∩ B) ∩ (A ∩ C)
B: (A ∪ B ) ∪ C
C: (A ∪ B) ∩ (A ∪ C)
D: B ∩ A ∪ C

Answer: (A ∪ B ) ∪ C

#### 27. 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

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

#### 28. 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

#### 29. 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

#### 30. 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

#### 31. 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

Answer: In each, no precise mathematical model of problem is acquired

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

A: subnormal fuzzy sets
B: normal fuzzy set
C: convex fuzzy set
D: concave fuzzy set

#### 33. —– defines logic funtion of two prepositions

A: prepositions
B: Linguistic hedges
C: truth tables
D: inference rules

#### 34. 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

#### 35. Multiple conjuctives antecedents is method of —– in FLC

A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above

#### 36. Multiple disjuctives antecedents is method of —– in FLC

A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above

#### 37. 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

#### 38. 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

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

#### 40. 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

#### 41. What Is Another Name For Fuzzy Inference Systems?

A: Fuzzy Expert system
B: Fuzzy Modelling
C: Fuzzy Logic Controller
D: All of the above

#### 42. 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

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

#### 44. 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

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

#### 45. 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

Answer: uses recombination such as cross over to produce offspring

#### 46. 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: deviation in step sizes remain static

#### 47. 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: deviation in step sizes change over time using some deterministic function

#### 48. 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: deviation in step size change dynamically

#### 49. 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

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

#### 51. 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: Individual solution is represented as a Finite State Machine

#### 52. 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: Individuals are represented by real-valued vector

#### 53. (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

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

#### 54. (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: λ mutants can be generated from one parent

#### 55. Termination condition for EA

A: maximally allowed CPU time is elapsed
B: total number of fitness evaluations reaches a given limit
C: population diversity drops under a given threshold
D: All the mentioned

#### 56. Which of the following operator is simplest selection operator?

A: Random selection
B: Proportional selection
C: tournament selection
D: none

#### 57. Which crossover operators are used in evolutionary programming?

A: Single-point crossover
B: two-point crossover
C: Uniform crossover
D: evolutionary programming does not use crossover operators

Answer: Revolutionary programming doesnot use crossover operators

#### 58. (1+1) ES

A: Operates on the population size of two
B: operates on population size of one
C: operates on population size of zero
D: operates on population size of λ

Answer: Operates on the population size of two

#### 59. Which of these emphasize of development of behavioral models?

A: Evolutionary programming
B: Genetic programming
C: Genetic algorithm
D: All the mentioned

#### 60. EP applies which evolutionary operators?

A: variation through application of mutation operators
B: selection
C: both a and b
D: none of the mentioned