1. GA stands for
A: genetic algorithm
B: genetic assurance
C: genese algorithm
D: none of these
Answer: genetic algorithm
2. LCS stands for
A: learning classes system
B: learning classifier systems
C: learned class system
D: none of these
Answer: learning classifier systems
3. GBML stands for
A: Genese based Machine learning
B: Genes based mobile learning
C: Genetic based machine learning
D: none of these
Answer: Genetic based machine learning
4. EV is dominantly used for solving ___.
A: optimization problems
B: NP problem
C: simple problems
D: none of these
Answer: optimization problems
5. EV is considered as?
A: adaptive
B: complex
C: both a and b
D: none of these
Answer: both a and b
6. Parameters that affect GA
A: initial population
B: selection process
C: fitness function
D: all of these
Answer: all of these
7. Fitness function should be
A: maximum
B: minimum
C: intermediate
D: none of these
Answer: minimum
8. Genetic algorithms are example of
A: heuristic
B: Evolutionary algorithm
C: ACO
D: PSO
Answer: Evolutionary algorithm
9. Applying recombination and mutation leads to a set of new candidates, called as?
A: sub parents
B: parents
C: offsprings
D: grandchild
ANSWER: offsprings
10. ____ decides who becomes parents and how many children the parents have.
A: parent combination
B: Parent selection
C: Parent mutation
D: Parent replace
Answer: Parent selection
11. Basic elements of EA are?
A: Parent Selection methods
B: Survival Selection methods
C: both a and b
D: none of these
Answer: both a and b
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
Answer: Hedges
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
Answer: 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
Answer: (A) 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
Answer: both associative & distributive
16. 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: Self Organization
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
Answer: 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
Answer: serial or parallel
20. What is ART in neural networks?
A: automatic resonance theory
B: artificial resonance theory
C: adaptive resonance theory
D: none of the mentioned
Answer: adaptive resonance theory
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
Answer: Degree of truth
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
Answer: conditional unqualified 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
Answer: 2 and 3
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
Answer: Fuzzy ≈ Forecasting
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
Answer: normal fuzzy set
33. —– defines logic funtion of two prepositions
A: prepositions
B: Linguistic hedges
C: truth tables
D: inference rules
Answer: truth tables
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
Answer: Fuzzy quantifiers
35. Multiple conjuctives antecedents is method of —– in FLC
A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above
Answer: decomposition rule
36. Multiple disjuctives antecedents is method of —– in FLC
A: decomposition rule
B: formation of rule
C: truth tables
D: All of the above
Answer: decomposition rule
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
Answer: rule in zero-order FIS
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
Answer: subnormal 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
Answer: Mamdani-type and Sugeno-type
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
Answer: 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
Answer: Probabilistic selection (μ+μ) selection
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
Answer: 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
Answer: All the mentioned
56. Which of the following operator is simplest selection operator?
A: Random selection
B: Proportional selection
C: tournament selection
D: none
Answer: Random selection
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
Answer: Evolutionary programming
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
Answer: both a and b
61. Which selection strategy works with negative fitness value?
A: Roulette wheel selection
B: Stochastic universal sampling
C: tournament selection
D: Rank selection
Answer: Rank selection
