| 1. | When we say that the boundary is crisp |
| A. | distinguish two regio |
| B. | cannot distinguis |
| C. | collection of ordere |
| D. | none of these |
| Answer» A. distinguish two regio | |
| 2. | In computing the output is called as |
| A. | consequent |
| B. | outfeed |
| C. | anticedents |
| D. | premise |
| Answer» A. consequent | |
| 3. | Fuzzy logic is a form of |
| A. | two valued logic |
| B. | crisp set logic |
| C. | many value logic |
| D. | binary set logic |
| Answer» C. many value logic | |
| 4. | Control actions while computing should be |
| A. | ambiguous |
| B. | unambioguos |
| C. | inaccurate |
| D. | none of these |
| Answer» B. unambioguos | |
| 5. | Core of soft computing is |
| A. | fuzzy computing,neu |
| B. | fuzzy network an |
| C. | neural science |
| D. | genetic science |
| Answer» A. fuzzy computing,neu | |
| 6. | Hard computing perfforms what type of computation |
| A. | sequential |
| B. | parallel |
| C. | approxiamate |
| D. | both a and b |
| Answer» A. sequential | |
| 7. | Who iniated idea of sofft computing |
| A. | charles darwin |
| B. | rich and berg |
| C. | mc culloch |
| D. | lofti a zadeh |
| Answer» D. lofti a zadeh | |
| 8. | Soft computing is based on |
| A. | fuzzy logic |
| B. | neural science |
| C. | crisp software |
| D. | binary logic |
| Answer» A. fuzzy logic | |
| 9. | In soft computing the problems,algorithms can be |
| A. | non adaptive |
| B. | adaptive |
| C. | static |
| D. | all of the above |
| Answer» B. adaptive | |
| 10. | Fuzzy Computing |
| A. | mimics human behav |
| B. | deals with inpreci |
| C. | exact information |
| D. | both a and b |
| Answer» D. both a and b | |
| 11. | Hard computing is also called as |
| A. | evolutionary comput |
| B. | conventional com |
| C. | non conventional co |
| D. | probablistic computing |
| Answer» B. conventional com | |
| 12. | Which computing produces accurate results |
| A. | soft computing |
| B. | hard computing |
| C. | both a and b |
| D. | none of the above |
| Answer» B. hard computing | |
| 13. | Neural network computing |
| A. | mimics human behav |
| B. | information proce |
| C. | both a and b |
| D. | none of the above |
| Answer» C. both a and b | |
| 14. | Artificial neural network is used for |
| A. | pattern recognition |
| B. | classification |
| C. | clustering |
| D. | all of the above |
| Answer» D. all of the above | |
| 15. | How does blind search differ from optimization |
| A. | blind search represe |
| B. | blind search usua |
| C. | blind search cannot |
| D. | none of these |
| Answer» B. blind search usua | |
| 16. | In modeling,an optimal solution is understood to be |
| A. | a solution that can o |
| B. | a solution found i |
| C. | a solution that is th |
| D. | a solution that require |
| Answer» C. a solution that is th | |
| 17. | When is a complete enumeration of solution used? |
| A. | when a solution that |
| B. | when there is en |
| C. | when the modeler |
| D. | when there are an infi |
| Answer» B. when there is en | |
| 18. | All of the follwing are true about heuristics EXCEPT |
| A. | heuristics are used w |
| B. | heuristics are use |
| C. | heuristics are used |
| D. | heuristics are rules of |
| Answer» C. heuristics are used | |
| 19. | Which approach is most suited to structured problem with little uncertainity |
| A. | simuation |
| B. | human intuition |
| C. | optimization |
| D. | genetic algorithm |
| Answer» C. optimization | |
| 20. | Genetic algorithm belong to the family of method in the |
| A. | artifical intelligence a |
| B. | optimization area |
| C. | complete enumerat |
| D. | non computer based i |
| Answer» A. artifical intelligence a | |
| 21. | What does the 0 membership value means in the set |
| A. | the object is fully insi |
| B. | the object is not i |
| C. | the object is partiall |
| D. | none of the above |
| Answer» B. the object is not i | |
| 22. | The union of two fuzzy sets is the of each element from two sets |
| A. | maximum |
| B. | minimum |
| C. | equal to |
| D. | not equal to |
| Answer» A. maximum | |
| 23. | The process of fuzzy interference system involes |
| A. | membership function |
| B. | fuzzy logic operat |
| C. | if-then rules |
| D. | all the above |
| Answer» D. all the above | |
| 24. | What does a fuzzifier do |
| A. | coverts crisp input to |
| B. | coverts crisp oupu |
| C. | coverts fuzzy input |
| D. | coverts fuzzy output to |
| Answer» A. coverts crisp input to | |
| 26. | Which of the following is/are type of fuzzy interference method |
| A. | mamdani |
| B. | sugeno |
| C. | rivest |
| D. | only a and b |
| Answer» D. only a and b | |
| 27. | A Fuzzy rule can have |
| A. | multiple part of ante |
| B. | only single part of |
| C. | multiple part of ant |
| D. | only single part of ante |
| Answer» C. multiple part of ant | |
| 28. | The a cut of a fuzzy set A is a crisp set defined by :- |
| A. | {x|ua(x)>a} |
| B. | {x|ua(x)>=a} |
| C. | {x|ua(x)<a} |
| D. | {x|ua(x)<=a} |
| Answer» B. {x|ua(x)>=a} | |
| 29. | The bandwidth(A) in a fuzzy set is given by |
| A. | (a)=|x1*x2| |
| B. | (a)=|x1+x2| |
| C. | (a)=|x1-x2| |
| D. | (a)=|x1/x2| |
| Answer» C. (a)=|x1-x2| | |
| 30. | The intersection of two fuzzy sets is the of each element from two sets |
| A. | maximum |
| B. | minimum |
| C. | equal to |
| D. | not equal to |
| Answer» B. minimum | |
| 31. | A={1/a,0.3/b,0.2/c,0.8/d,0/e} B={0.6/a,0.9/b,0.1/c,0.3/d,0.2/e} What will be the co |
| A. | m{0/a,0.7/b,0.8/c,0.2/ |
| B. | {0/a,0.9/b,0.7/c,0 |
| C. | {0.8/a,0.7/b,0.8/c,0 |
| D. | {0/a,0.7/b,0.8/c,0.9/d, |
| Answer» A. m{0/a,0.7/b,0.8/c,0.2/ | |
| 32. | A={1/a,0.3/b,0.2/c,0.8/d,0/e} B={0.6/a,0.9/b,0.1/c,0.3/d,0.2/e} What will be the uni |
| A. | {1/a,0.9/b,0.1/c,0.5/ |
| B. | {0.8/a,0.9/b,0.2/c |
| C. | {1/a,0.9/b,0.2/c,0.8 |
| D. | {1/a,0.9/b,0.2/c,0.8/d, |
| Answer» C. {1/a,0.9/b,0.2/c,0.8 | |
| 33. | A={1/a,0.3/b,0.2/c,0.8/d,0/e} B={0.6/a,0.9/b,0.1/c,0.3/d,0.2/e} What will be the inte |
| A. | {0.6/a,0.3/b,0.1/c,0.3 |
| B. | {0.6/a,0.8/b,0.1/c |
| C. | {0.6/a,0.3/b,0.1/c,0 |
| D. | {0.6/a,0.3/b,0.2/c,0.3/ |
| Answer» A. {0.6/a,0.3/b,0.1/c,0.3 | |
| 34. | What denotes the support(A) in a fuzzy set? |
| A. | {x|ua(x)>0} |
| B. | {x|ua(x)<0} |
| C. | {x|ua(x)<=0} |
| D. | {x|ua(x)<0.5} |
| Answer» A. {x|ua(x)>0} | |
| 35. | What denotes the core(A) in a fuzzy set? |
| A. | {x|ua(x)>0} |
| B. | {x|ua(x)=1} |
| C. | {x|ua(x)>=0.5} |
| D. | {x|ua(x)>0.8} |
| Answer» B. {x|ua(x)=1} | |
| 36. | Fuzzy logic deals with which of the following |
| A. | fuzzy set |
| B. | fuzzy algebra |
| C. | both a and b |
| D. | none of the above |
| Answer» C. both a and b | |
| 37. | which of the following is a sequence of steps taken in designning a fuzy logic machine |
| A. | fuzzification->rule ev |
| B. | deffuzification->r |
| C. | rule evaluation->fuz |
| D. | rule evaluation->defuz |
| Answer» A. fuzzification->rule ev | |
| 38. | can a crisp set be a fuzzy set? |
| A. | no |
| B. | yes |
| C. | depends |
| D. | all of the above |
| Answer» B. yes | |
| 39. | All of the follwing are suitable problem for genetic algorithm EXCEPT |
| A. | pattern recognization |
| B. | simulation of biol |
| C. | simple optimization |
| D. | dynamic process contr |
| Answer» C. simple optimization | |
| 40. | Tabu search is an example of ? |
| A. | heuristic |
| B. | evolutionary algo |
| C. | aco |
| D. | pso |
| Answer» A. heuristic | |
| 41. | Genetic algorithms are example of |
| A. | heuristic |
| B. | evolutionary algo |
| C. | aco |
| D. | pso |
| Answer» B. evolutionary algo | |
| 42. | mutation is applied on candidates. |
| A. | one |
| B. | two |
| C. | more than two |
| D. | noneof these |
| Answer» A. one | |
| 43. | recombination is applied on candidates. |
| A. | one |
| B. | two |
| C. | more than two |
| D. | noneof these |
| Answer» B. two | |
| 44. | LCS belongs to based methods? |
| A. | rule based learning |
| B. | genetic learning |
| C. | both a and b |
| D. | noneof these |
| Answer» A. rule based learning | |
| 45. | Survival is approach. |
| A. | deteministic |
| B. | non deterministic |
| C. | semi deterministic |
| D. | noneof these |
| Answer» A. deteministic | |
| 46. | Evolutionary algorithms are a based approach |
| A. | heuristic |
| B. | metaheuristic |
| C. | both a and b |
| D. | noneof these |
| Answer» A. heuristic | |
| 47. | Idea of genetic algorithm came from |
| A. | machines |
| B. | birds |
| C. | aco |
| D. | genetics |
| Answer» D. genetics | |
| 48. | Chromosomes are actually ? |
| A. | line representation |
| B. | string representa |
| C. | circular representat |
| D. | all of these |
| Answer» B. string representa | |
| 49. | what are the parameters that affect GA are/is |
| A. | selection process |
| B. | initial population |
| C. | both a and b |
| D. | none of these |
| Answer» C. both a and b | |
| 51. | Evolution Strategies is developed with |
| A. | selection |
| B. | mutation |
| C. | a population of size |
| D. | all of these |
| Answer» D. all of these | |
| 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 | |
| 53. | in ES survival is |
| A. | indeterministic |
| B. | deterministic |
| C. | both a and b |
| D. | none of these |
| Answer» D. none of these | |
| 54. | What is the first step in Evolutionary algorithm |
| A. | termination |
| B. | selection |
| C. | recombination |
| D. | initialization |
| Answer» D. initialization | |
| 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 | |
| 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 | |
| 57. | Determining the duration of the simulation occurs before the model is validated and te |
| A. | true |
| B. | false |
| Answer» B. false | |
| 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 | |
| 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 | |
| 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 | |
| 61. | EC stands for? |
| A. | evolutionary comput |
| B. | evolutionary com |
| C. | electronic computa |
| D. | noneof these |
| Answer» A. evolutionary comput | |
| 62. | GA stands for |
| A. | genetic algorithm |
| B. | genetic asssuranc |
| C. | genese alforithm |
| D. | noneof these |
| Answer» A. genetic algorithm | |
| 63. | LCS stands for |
| A. | learning classes syste |
| B. | learning classifier |
| C. | learned class syste |
| D. | mnoneof these |
| Answer» B. learning classifier | |
| 64. | GBML stands for |
| A. | genese based machi |
| B. | genes based mob |
| C. | genetic bsed machi |
| D. | noneof these |
| Answer» C. genetic bsed machi | |
| 65. | EV is dominantly used for solving . |
| A. | optimization proble |
| B. | mnp problem |
| C. | simple problems |
| D. | noneof these |
| Answer» A. optimization proble | |
| 66. | EV is considered as? |
| A. | adaptive |
| B. | complex |
| C. | both a and b |
| D. | noneof these |
| Answer» C. both a and b | |
| 67. | Parameters that affect GA |
| A. | initial population |
| B. | selection process |
| C. | fitness function |
| D. | all of these |
| Answer» D. all of these | |
| 68. | Fitness function should be |
| A. | maximum |
| B. | minimum |
| C. | intermediate |
| D. | noneof these |
| Answer» B. minimum | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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) | |
| 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. | |
| 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 | |
| 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 | |
| 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, | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 92. | —– defines logic funtion of two prepositions |
| A. | prepositions |
| B. | lingustic hedges |
| C. | truth tables |
| D. | inference rules |
| Answer» C. truth tables | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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. | |
| 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. | |
| 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 | |
| 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 | |
| 101. | 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» A. probabilistic selection (μ+μ) selection | |
| 102. | 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» B. (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children | |
| 103. | In Evolutionary programming, recombination is |
| A. | doesnot 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» A. doesnot use recombination to produce offspring. it only uses mutation | |
| 104. | In Evolutionary strategy, recombination is |
| A. | doesnot 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» B. uses recombination such as cross over to produce offspring | |
| 105. | 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» A. deviation in step sizes remain static | |
| 106. | 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» B. deviation in step sizes change over time using some deterministic function | |
| 107. | 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» C. deviation in step size change dynamically | |
| 108. | 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» D. 1 and 4 | |
| 109. | 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» B. (µ+λ): select survivors among parents and offspring | |
| 110. | 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» B. individual solution is represented as a finite state machine | |
| 111. | 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» A. individuals are represented by real- valued vector | |
| 112. | (1+1) ES |
| A. | offspring becomes parent if offspring\s fitness is as good as parent of next generation |
| B. | offspring become parent by default |
| C. | offspring never becomes parent |
| D. | none of the mentioned |
| Answer» A. offspring becomes parent if offspring\s fitness is as good as parent of next generation | |
| 113. | (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» A. λ mutants can be generated from one parent | |
| 114. | 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 |
| Answer» D. all the mentioned | |
| 115. | Which of the following operator is simplest selection operator? |
| A. | random selection |
| B. | proportional selection |
| C. | tournament selection |
| D. | none |
| Answer» A. random selection | |
| 116. | 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 |
| Answer» D. evolutionary programming doesnot use crossover operators | |
| 117. | (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 |
| Answer» A. operates on population size of two | |
| 118. | Which of these emphasize of development of behavioral models? |
| A. | evolutionary programming |
| B. | genetic programming |
| C. | genetic algorithm |
| D. | all the mentioned |
| Answer» A. evolutionary programming | |
| 119. | 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» C. both a and b | |
| 120. | Which selection strategy works with negative fitness value? |
| A. | roulette wheel selection |
| B. | stochastic universal sampling |
| C. | tournament selection |
| D. | rank selection |
| Answer» D. rank selection | |
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