390+ Soft Computing and Optimization Algorithms Solved MCQs

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1.

When we say that the boundary is crisp

A. distinguish two regions clearly
B. cannot distinguish two regions clearly
C. collection of ordered pairs
D. none of these
Answer» A. distinguish two regions clearly
discuss
2.

In computing the output is called as

A. consequent
B. outfeed
C. anticedents
D. premise
Answer» A. consequent
discuss
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
discuss
4.

Control actions while computing should be

A. ambiguous
B. unambioguos
C. inaccurate
D. none of these
Answer» B. unambioguos
discuss
5.

Core of soft computing is

A. fuzzy computing,neural computing,genetic algorithm
B. fuzzy network and artificial intelligence
C. neural science
D. genetic science
Answer» A. fuzzy computing,neural computing,genetic algorithm
discuss
6.

Hard computing perfforms what type of computation

A. sequential
B. parallel
C. approxiamate
D. both a and b
Answer» A. sequential
discuss
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
discuss
8.

Soft computing is based on

A. fuzzy logic
B. neural science
C. crisp software
D. binary logic
Answer» A. fuzzy logic
discuss
9.

In soft computing the problems,algorithms can be

A. non adaptive
B. adaptive
C. static
D. all of the above
Answer» B. adaptive
discuss
10.

Fuzzy Computing

A. mimics human behaviour
B. deals with inprecise,probablistic
C. exact information
D. both a and b
Answer» D. both a and b
discuss
11.

Hard computing is also called as

A. evolutionary computing
B. conventional computing
C. non conventional computing
D. probablistic computing
Answer» B. conventional computing
discuss
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
discuss
13.

Neural network computing

A. mimics human behaviour
B. information processing paradigm
C. both a and b
D. none of the above
Answer» C. both a and b
discuss
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
discuss
15.

How does blind search differ from optimization

A. blind search represent a guided approach while optimization is unguided
B. blind search usually does not conclude in one step like some optimization methods.
C. blind search cannot result in optimal solution whereas optimization method do
D. none of these
Answer» B. blind search usually does not conclude in one step like some optimization methods.
discuss
16.

In modeling,an optimal solution is understood to be

A. a solution that can only be determined by an exhaustive enumeration testing of alternatives
B. a solution found in the least possible time and using the least possible computing resources
C. a solution that is the best based on criteria defined in the design phase
D. a solution that requires an algorithm for the determination
Answer» C. a solution that is the best based on criteria defined in the design phase
discuss
17.

When is a complete enumeration of solution used?

A. when a solution that is "good enough" is fine and good heuristics are available
B. when there is enough time and computational power available
C. when the modeler requires a guided approach to problem solving
D. when there are an infinite number of solution to be searched
Answer» B. when there is enough time and computational power available
discuss
18.

All of the follwing are true about heuristics EXCEPT

A. heuristics are used when the modeler requires a guided approach to problem solving
B. heuristics are used when a solution that is "good enough" is sought
C. heuristics are used when there is abundant time and computational power
D. heuristics are rules of good judgement
Answer» C. heuristics are used when there is abundant time and computational power
discuss
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
discuss
20.

Genetic algorithm belong to the family of method in the

A. artifical intelligence area
B. optimization area
C. complete enumeration family of methods
D. non computer based isolation area
Answer» A. artifical intelligence area
discuss
21.

What does the 0 membership value means in the set

A. the object is fully inside the set
B. the object is not in the set
C. the object is partially present in the set
D. none of the above
Answer» B. the object is not in the set
discuss
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
discuss
23.

The process of fuzzy interference system involes

A. membership functions
B. fuzzy logic operators
C. if-then rules
D. all the above
Answer» D. all the above
discuss
24.

What does a fuzzifier do

A. coverts crisp input to linguistic variables
B. coverts crisp ouput to linguistic variables
C. coverts fuzzy input to linguistic variables
D. coverts fuzzy output to linguistic variables
Answer» A. coverts crisp input to linguistic variables
discuss
25.

Which of the folloowing is not defuzzifier method

A. centroid of area
B. mean of maximum
C. largest of maximum
D. hypotenuse of triangle
Answer» D. hypotenuse of triangle
discuss
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
discuss
27.

A Fuzzy rule can have

A. multiple part of antecedent,only single part of consequent
B. only single part of antecedent,mutiple part of consequent
C. multiple part of antecedent,multiple part of consequent
D. only single part of antecedent,only single part of consequent
Answer» C. multiple part of antecedent,multiple part of consequent
discuss
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}
discuss
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|
discuss
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
discuss
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 complement of A?

A. {0/a,0.7/b,0.8/c,0.2/d,1/e}
B. {0/a,0.9/b,0.7/c,0.2/d,1/e}
C. {0.8/a,0.7/b,0.8/c,0.7/d,1/e}
D. {0/a,0.7/b,0.8/c,0.9/d,1/e}
Answer» A. {0/a,0.7/b,0.8/c,0.2/d,1/e}
discuss
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 union of AUB?

A. {1/a,0.9/b,0.1/c,0.5/d,0.2/e}
B. {0.8/a,0.9/b,0.2/c,0.5/d,0.2/e}
C. {1/a,0.9/b,0.2/c,0.8/d,0.2/e}
D. {1/a,0.9/b,0.2/c,0.8/d,0.8/e}
Answer» C. {1/a,0.9/b,0.2/c,0.8/d,0.2/e}
discuss
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 intersection of A and B ?

A. {0.6/a,0.3/b,0.1/c,0.3/d,0/e}
B. {0.6/a,0.8/b,0.1/c,0.3/d,0/e}
C. {0.6/a,0.3/b,0.1/c,0.5/d,0/e}
D. {0.6/a,0.3/b,0.2/c,0.3/d,1/e}
Answer» A. {0.6/a,0.3/b,0.1/c,0.3/d,0/e}
discuss
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}
discuss
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}
discuss
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
discuss
37.

which of the following is a sequence of steps taken in designning a fuzy logic machine

A. fuzzification->rule evaluation->deffuzification
B. deffuzification->rule evaluation->fuzzification
C. rule evaluation->fuzzification->deffuzification
D. rule evaluation->defuzzification->fuzzification
Answer» A. fuzzification->rule evaluation->deffuzification
discuss
38.

can a crisp set be a fuzzy set?

A. no
B. yes
C. depends
D. all of the above
Answer» B. yes
discuss
39.

All of the follwing are suitable problem for genetic algorithm EXCEPT

A. pattern recognization
B. simulation of biological models
C. simple optimization with few variables
D. dynamic process control
Answer» C. simple optimization with few variables
discuss
40.

Tabu search is an example of ?

A. heuristic
B. evolutionary algorithm
C. aco
D. pso
Answer» A. heuristic
discuss
41.

Genetic algorithms are example of

A. heuristic
B. evolutionary algorithm
C. aco
D. pso
Answer» B. evolutionary algorithm
discuss
42.

mutation is applied on __candidates.

A. one
B. two
C. more than two
D. noneof these
Answer» A. one
discuss
43.

recombination is applied on __candidates.

A. one
B. two
C. more than two
D. noneof these
Answer» B. two
discuss
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
discuss
45.

Survival is ___ approach.

A. deteministic
B. non deterministic
C. semi deterministic
D. noneof these
Answer» A. deteministic
discuss
46.

Evolutionary algorithms are a ___ based approach

A. heuristic
B. metaheuristic
C. both a and b
D. noneof these
Answer» A. heuristic
discuss
47.

Idea of genetic algorithm came from

A. machines
B. birds
C. aco
D. genetics
Answer» D. genetics
discuss
48.

Chromosomes are actually ?

A. line representation
B. string representation
C. circular representation
D. all of these
Answer» B. string representation
discuss
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
discuss
50.

Evolutionary programming was developef by

A. fredrik
B. fodgel
C. frank
D. flin
Answer» B. fodgel
discuss
51.

Evolution Strategies is developed with

A. selection
B. mutation
C. a population of size one
D. all of these
Answer» D. all of these
discuss
52.

Evolution Strategies typically uses

A. real-valued vector representations
B. vector representation
C. time based representation
D. none of these
Answer» A. real-valued vector representations
discuss
53.

in ES survival is

A. indeterministic
B. deterministic
C. both a and b
D. none of these
Answer» D. none of these
discuss
54.

What is the first step in Evolutionary algorithm

A. termination
B. selection
C. recombination
D. initialization
Answer» D. initialization
discuss
55.

Elements of ES are/is

A. parent population size
B. survival population size
C. both a and b
D. none of these
Answer» C. both a and b
discuss
56.

What are different types of crossover

A. discrete and intermedium
B. discrete and continuous
C. continuous and intemedium
D. none of these
Answer» A. discrete and intermedium
discuss
57.

Determining the duration of the simulation occurs before the model is validated and tested.

A. true
B. false
Answer» B. false
discuss
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
discuss
59.

Discrete events and agent-based models are usuallly used for_____________.

A. middle or low level of abstractions
B. high level of abstraction
C. very high level of abstraction
D. none of these
Answer» A. middle or low level of abstractions
discuss
60.

_____doesnot usually allow decision makers to see how a solution to a ___________envolves over time nor can decision makers interact with it.

A. simulation ,complex problem
B. simulation,easy problem
C. genetics,complex problem
D. genetics,easy problem
Answer» A. simulation ,complex problem
discuss
61.

EC stands for?

A. evolutionary computatons
B. evolutionary computer
C. electronic computations
D. noneof these
Answer» A. evolutionary computatons
discuss
62.

GA stands for

A. genetic algorithm
B. genetic asssurance
C. genese alforithm
D. noneof these
Answer» A. genetic algorithm
discuss
63.

LCS stands for

A. learning classes system
B. learning classifier systems
C. learned class system
D. noneof these
Answer» B. learning classifier systems
discuss
64.

GBML stands for

A. genese based machine learning
B. genes based mobile learning
C. genetic bsed machine learning
D. noneof these
Answer» C. genetic bsed machine learning
discuss
65.

EV is dominantly used for solving ___.

A. optimization problems
B. np problem
C. simple problems
D. noneof these
Answer» A. optimization problems
discuss
66.

EV is considered as?

A. adaptive
B. complex
C. both a and b
D. noneof these
Answer» C. both a and b
discuss
67.

Parameters that affect GA

A. initial population
B. selection process
C. fitness function
D. all of these
Answer» D. all of these
discuss
68.

Fitness function should be

A. maximum
B. minimum
C. intermediate
D. noneof these
Answer» B. minimum
discuss
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
discuss
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
discuss
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
discuss
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
discuss
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
discuss
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
discuss
75.

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» C. both associative & distributive
discuss
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
discuss
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
discuss
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
discuss
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
discuss
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
discuss
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
discuss
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. 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» C. {(l, 1), (2, 1), (3, 0.3), (4, 1), (6,0.2), (7, 1)}
discuss
83.

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» A. fuzzification → rule evaluation → defuzzification
discuss
84.

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» C. {0.1, 0.5, 0.4, 0.2, 0.2}
discuss
85.

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» D. {(0.3,12), (0.5,13), (0.6,14), (1,15), (0.7,16), (0.5,17), (0.2,18)}
discuss
86.

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
discuss
87.

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» C. {2, 3, 4, 5, 6, 7, 8, 9, 10}
discuss
88.

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» A. conditional unqualified proposition
discuss
89.

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
discuss
90.

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» B. fuzzy ≈ forecasting
discuss
91.

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» C. in each, no precise mathematical model of problem is acquired
discuss
92.

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
discuss
93.

----- defines logic funtion of two prepositions

A. prepositions
B. lingustic hedges
C. truth tables
D. inference rules
Answer» C. truth tables
discuss
94.

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
discuss
95.

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
discuss
96.

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
discuss
97.

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
discuss
98.

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.
discuss
99.

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.
discuss
100.

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
discuss

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