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390+ Soft Computing and Optimization Algorithms Solved MCQs

These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Uncategorized topics .

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
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,neural computing,genetic algorithm
B. fuzzy network and artificial intelligence
C. neural science
D. genetic science
Answer» A. fuzzy computing,neural computing,genetic algorithm
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 behaviour
B. deals with inprecise,probablistic
C. exact information
D. both a and b
Answer» D. both a and b
11.

Hard computing is also called as

A. evolutionary computing
B. conventional computing
C. non conventional computing
D. probablistic computing
Answer» B. conventional computing
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 behaviour
B. information processing paradigm
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 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.
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
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
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
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 area
B. optimization area
C. complete enumeration family of methods
D. non computer based isolation area
Answer» A. artifical intelligence area
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
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 functions
B. fuzzy logic operators
C. if-then rules
D. all the above
Answer» D. all the above
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
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
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 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
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 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}
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}
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}
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 evaluation->deffuzification
B. deffuzification->rule evaluation->fuzzification
C. rule evaluation->fuzzification->deffuzification
D. rule evaluation->defuzzification->fuzzification
Answer» A. fuzzification->rule evaluation->deffuzification
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 biological models
C. simple optimization with few variables
D. dynamic process control
Answer» C. simple optimization with few variables
40.

Tabu search is an example of ?

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

Genetic algorithms are example of

A. heuristic
B. evolutionary algorithm
C. aco
D. pso
Answer» B. evolutionary algorithm
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 representation
C. circular representation
D. all of these
Answer» B. string representation
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
50.

Evolutionary programming was developef by

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

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