McqMate
| 301. |
What are the applications of Fuzzy Inference Systems? |
| A. | Wireless services, heat control and printers |
| B. | Restrict power usage, telephone lines and sort data |
| C. | Simulink, boiler and CD recording |
| D. | Automatic control, decision analysis and data classification |
| Answer» D. Automatic control, decision analysis and data classification | |
| 302. |
Which of the following is not true regarding the principles of fuzzy logic ? |
| A. | Fuzzy logic follows the principle of Aristotle and |
| B. | Japan is currently the most active users of fuzzy logic |
| C. | Fuzzy logic is a concept of 'certain degree' |
| D. | Boolean logic is a subset of fuzzy logic |
| Answer» A. Fuzzy logic follows the principle of Aristotle and | |
| 303. |
Suppose, a fuzzy set Young is defined as follows Young = (10, 0.5), (20, 0.8), (30, 0.8), (40, 0.5), (50, 0.3) Then the crisp value of Young using MoM method is |
| A. | 20 |
| B. | 25 |
| C. | 30 |
| D. | 35 |
| Answer» B. 25 | |
| 304. |
What Is Fuzzy Inference Systems? |
| A. | The process of formulating the mapping from a given input to an output using fuzzy |
| B. | The process of formulating the mapping from a given input to an output using fuzzy |
| C. | Having a larger output than the input |
| D. | Having a smaller output than the input |
| Answer» A. The process of formulating the mapping from a given input to an output using fuzzy | |
| 305. |
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 a television and remote combination 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 feul 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 | |
| 306. |
R=(AXB)U(ÃXY) is |
| A. | Zadeh's Max Product rule for If x is A then y is B else y is C |
| B. | Zadeh's Max Min rule for If x is A then y is B |
| C. | Zadeh's Max Product rule for If x is A then y is B else y is C |
| D. | Zadeh's Max Min rule for If x is A then y is B |
| Answer» D. Zadeh's Max Min rule for If x is A then y is B | |
| 307. |
Sequence of steps in EA |
| A. | initialization-> selection- >mutation- >crossover- >termination |
| B. | initialization-> selection- >crossover- >termination |
| C. | initialization-> selection->crossover- >mutation- >termination |
| D. | None of these |
| Answer» C. initialization-> selection->crossover- >mutation- >termination | |
| 308. |
How many genes will be in the alphabet of the algorithm? |
| A. | n*(n-1)/2 |
| B. | n*(n+1)/2 |
| C. | n*(n-2)/2 |
| D. | n*(n+2)/2 |
| Answer» A. n*(n-1)/2 | |
| 309. |
Which of the following is not true for Genetic algorithms? |
| A. | It is a probabilistic search algorithm |
| B. | It is guaranteed to give global optimum solutions |
| C. | If an optimization problem has more than one solution, then it will return all the solutions |
| D. | It is an iterative process suitable for parallel programming |
| Answer» B. It is guaranteed to give global optimum solutions | |
| 310. |
Which one of the following is not necessarily be considered as GA parameters? |
| A. | the population size. |
| B. | the obtainable accuracy |
| C. | the mutation probability |
| D. | the average fitness score |
| Answer» D. the average fitness score | |
| 311. |
Which of the following optimization problem(s) can be better solved with Order GA? |
| A. | 0-1 Knapsack problem |
| B. | Travelling salesman problem |
| C. | Job shop scheduling problem |
| D. | Optimal binary search tree construction problem |
| Answer» B. Travelling salesman problem | |
| 312. |
If crossover between chromosomes in search space does not produce significantly different offspring, what does it imply? (if offspring consist of one half of each parent)
|
| A. | ii, iii & iv only |
| B. | ii, iii only |
| C. | i, iii & iv only |
| D. | All of the mentioned |
| Answer» B. ii, iii only | |
| 313. |
In Rank‐based selection scheme, which of the following is not correct |
| A. | The % area to be occupied by an individual , is given by average of sumation of elements |
| B. | Two or more individuals with the same fitness values should have the same rank. |
| C. | Individuals are arranged in a descending order of their fitness values. |
| D. | The proportionate based selection scheme is followed based on the assigned rank. |
| Answer» C. Individuals are arranged in a descending order of their fitness values. | |
| 314. |
Real Coded GA flow is- |
| A. | Random mutation- Polynomial mutation |
| B. | Polynomial mutation-Random mutation |
| C. | Flipping-Random mutation- Polynomial mutation |
| D. | None |
| Answer» A. Random mutation- Polynomial mutation | |
| 315. |
Breeding in GA flow is- |
| A. | Create a mating pool- Select a pair- Reproduce |
| B. | Select a pair-Create a mating pool- Reproduce |
| C. | Reproduce-Create a mating pool- Select a pair |
| D. | None |
| Answer» A. Create a mating pool- Select a pair- Reproduce | |
| 316. |
Binary Coded GA flow is- |
| A. | Flipping- Interchanging- Reversing |
| B. | Reversing- Flipping- Interchanging- |
| C. | Interchanging- Reversing-Flipping |
| D. | None |
| Answer» A. Flipping- Interchanging- Reversing | |
| 317. |
Which of the following comparison is true? |
| A. | In the event of restricted accessto information, GAs win out in that they require much |
| B. | Under any circumstances, GAs always outperform other algorithms. |
| C. | The qualities of solutions offered by GAs for any problems are always better than |
| D. | GAs could be applied to any problem, whereas certain algorithms are applicable to |
| Answer» A. In the event of restricted accessto information, GAs win out in that they require much | |
| 318. |
Premature convergence of PSO is |
| A. | Once PSO traps in global optimum, it is dificult to jump out of global optimum |
| B. | Once PSO traps in local optimum, it is dificult to jump out of local optimum |
| C. | Once PSO traps in local optimum, it is dificult to jump out of global optimum |
| D. | Once PSO traps in global optimum, it is dificult to jump out of local optimum |
| Answer» B. Once PSO traps in local optimum, it is dificult to jump out of local optimum | |
| 319. |
Takugi-Sugeno approach to FLC design is computationally more expensive compared to Mamdani approach because |
| A. | Mamdani approach considers a less number of rules in fuzzy rule base |
| B. | Searching a rule in Mamdani approach is simple and hence less time consuming |
| C. | Takagi-Sugeno approach consider a large number of rules in fuzzy rule base |
| D. | Computation of each rule in Takagi-Sugeno approach is more time consuming |
| Answer» D. Computation of each rule in Takagi-Sugeno approach is more time consuming | |
| 320. |
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 | |
| 321. |
Core of soft computing is |
| A. | Fuzzy computing,neu |
| B. | Fuzzy network an |
| C. | Neural Science |
| D. | Genetic Science |
| Answer» A. Fuzzy computing,neu | |
| 322. |
Fuzzy Computing |
| A. | mimics human behav |
| B. | deals with inpreci |
| C. | exact information |
| D. | both a and b |
| Answer» D. both a and b | |
| 323. |
Hard computing is also called as |
| A. | evolutionary comput |
| B. | conventional com |
| C. | non conventional co |
| D. | probablistic computing |
| Answer» B. conventional com | |
| 324. |
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 | |
| 325. |
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 | |
| 326. |
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 | |
| 327. |
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 | |
| 328. |
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 | |
| 329. |
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 | |
| 330. |
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 | |
| 331. |
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 | |
| 332. |
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 | |
| 333. |
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 | |
| 334. |
Which of the folloowing is not defuzzifier method |
| A. | centroid of area |
| B. | mean of maximu |
| C. | largest of maximum |
| D. | hypotenuse of triangle |
| Answer» D. hypotenuse of triangle | |
| 335. |
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 | |
| 336. |
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 | |
| 337. |
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/ | |
| 338. |
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 | |
| 339. |
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 | |
| 340. |
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 | |
| 341. |
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 | |
| 342. |
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 | |
| 343. |
Tabu search is an example of ? |
| A. | heuristic |
| B. | Evolutionary algo |
| C. | ACO |
| D. | PSO |
| Answer» A. heuristic | |
| 344. |
Genetic algorithms are example of |
| A. | heuristic |
| B. | Evolutionary algo |
| C. | ACO |
| D. | PSO |
| Answer» B. Evolutionary algo | |
| 345. |
mutation is applied on candidates. |
| A. | one |
| B. | two |
| C. | more than two |
| D. | noneof these |
| Answer» A. one | |
| 346. |
recombination is applied on candidates. |
| A. | one |
| B. | two |
| C. | more than two |
| D. | noneof these |
| Answer» B. two | |
| 347. |
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 | |
| 348. |
Survival is approach. |
| A. | deteministic |
| B. | non deterministic |
| C. | semi deterministic |
| D. | noneof these |
| Answer» A. deteministic | |
| 349. |
Evolutionary algorithms are a based approach |
| A. | heuristic |
| B. | metaheuristic |
| C. | both a and b |
| D. | noneof these |
| Answer» A. heuristic | |
| 350. |
Chromosomes are actually ? |
| A. | line representation |
| B. | String representa |
| C. | Circular representat |
| D. | all of these |
| Answer» B. String representa | |
| 351. |
Evolution Strategies is developed with |
| A. | selection |
| B. | mutation |
| C. | a population of size |
| D. | all of these |
| Answer» D. all of these | |
| 352. |
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 | |
| 353. |
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 | |
| 354. |
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 | |
| 355. |
Determining the duration of the simulation occurs before the model is validated and te |
| A. | TRUE |
| B. | FALSE |
| Answer» B. FALSE | |
| 356. |
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 | |
| 357. |
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 | |
| 358. |
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 | |
| 359. |
EC stands for? |
| A. | Evolutionary Comput |
| B. | Evolutionary com |
| C. | Electronic computa |
| D. | noneof these |
| Answer» A. Evolutionary Comput | |
| 360. |
GA stands for |
| A. | genetic algorithm |
| B. | genetic asssuranc |
| C. | genese alforithm |
| D. | noneof these |
| Answer» A. genetic algorithm | |
| 361. |
LCS stands for |
| A. | learning classes syste |
| B. | learning classifier |
| C. | learned class syste |
| D. | mnoneof these |
| Answer» B. learning classifier | |
| 362. |
GBML stands for |
| A. | Genese based Machi |
| B. | Genes based mob |
| C. | Genetic bsed machi |
| D. | noneof these |
| Answer» C. Genetic bsed machi | |
| 363. |
EV is dominantly used for solving . |
| A. | optimization problem |
| B. | NP problem |
| C. | simple problems |
| D. | noneof these |
| Answer» A. optimization problem | |
| 364. |
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 | |
| 365. |
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 | |
| 366. |
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 | |
| 367. |
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 | |
| 368. |
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 | |
| 369. |
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 | |
| 370. |
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 | |
| 371. |
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 | |
| 372. |
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 | |
| 373. |
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) | |
| 374. |
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. | |
| 375. |
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 | |
| 376. |
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, | |
| 377. |
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 | |
| 378. |
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 | |
| 379. |
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 | |
| 380. |
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 | |
| 381. |
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 | |
| 382. |
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. | |
| 383. |
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 | |
| 384. |
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 | |
| 385. |
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 | |
| 386. |
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 | |
| 387. |
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 | |
| 388. |
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 | |
| 389. |
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 | |
| 390. |
(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 | |
| 391. |
(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 | |
| 392. |
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 | |
| 393. |
Which of the following operator is simplest selection operator? |
| A. | Random selection |
| B. | Proportional selection |
| C. | tournament selection |
| D. | none |
| Answer» A. Random selection | |
| 394. |
(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 | |
| 395. |
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 | |
| 396. |
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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