**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**