Computer Engineering Soft Computing Set 5

Computer Science Engineering (CSE)

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This set of Computer Engineering Soft Computing Multiple Choice Questions & Answers (MCQs) focuses on Computer Engineering Soft Computing Set 5

Q1 | In Evolutionary programming, survival selection is
  • probabilistic selection (μ+μ) selection
  • (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
  • children replace the parent
  • all the mentioned
Answer: probabilistic selection (μ+μ) selection
Q2 | In Evolutionary strategy, survival selection is
  • probabilistic selection (μ+μ) selection
  • (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
  • children replace the parent
  • all the mentioned
Answer: (μ, λ)- selection based on the children only (μ+λ)- selection based on both the set of parent and children
Q3 | In Evolutionary programming, recombination is
  • doesnot use recombination to produce offspring. it only uses mutation
  • uses recombination such as cross over to produce offspring
  • uses various recombination operators
  • none of the mentioned
Answer: doesnot use recombination to produce offspring. it only uses mutation
Q4 | In Evolutionary strategy, recombination is
  • doesnot use recombination to produce offspring. it only uses mutation
  • uses recombination such as cross over to produce offspring
  • uses various recombination operators
  • none of the mentioned
Answer: uses recombination such as cross over to produce offspring
Q5 | Step size in non-adaptive EP :
  • deviation in step sizes remain static
  • deviation in step sizes change over time using some deterministic function
  • deviation in step size change dynamically
  • size=1
Answer: deviation in step sizes remain static
Q6 | Step size in dynamic EP :
  • deviation in step sizes remain static
  • deviation in step sizes change over time using some deterministic function
  • deviation in step size change dynamically
  • size=1
Answer: deviation in step sizes change over time using some deterministic function
Q7 | Step size in self-adaptive EP :
  • deviation in step sizes remain static
  • deviation in step sizes change over time using some deterministic function
  • deviation in step size change dynamically
  • size=1
Answer: deviation in step size change dynamically
Q8 | What are normally the two best measurement units for an evolutionary algorithm?1. Number of evaluations2. Elapsed time3. CPU Time4. Number of generations
  • 1 and 2
  • 2 and 3
  • 3 and 4
  • 1 and 4
Answer: 1 and 4
Q9 | Evolutionary Strategies (ES)
  • (µ,λ): select survivors among parents and offspring
  • (µ+λ): select survivors among parents and offspring
  • (µ-λ): select survivors among offspring only
  • (µ:λ): select survivors among offspring only
Answer: (µ+λ): select survivors among parents and offspring
Q10 | In Evolutionary programming,
  • individuals are represented by real- valued vector
  • individual solution is represented as a finite state machine
  • individuals are represented as binary string
  • none of the mentioned
Answer: individual solution is represented as a finite state machine
Q11 | In Evolutionary Strategy,
  • individuals are represented by real- valued vector
  • individual solution is represented as a finite state machine
  • individuals are represented as binary string
  • none of the mentioned
Answer: individuals are represented by real- valued vector
Q12 | (1+1) ES
  • offspring becomes parent if offspring\s fitness is as good as parent of next generation
  • offspring become parent by default
  • offspring never becomes parent
  • none of the mentioned
Answer: offspring becomes parent if offspring\s fitness is as good as parent of next generation
Q13 | (1+λ) ES
  • λ mutants can be generated from one parent
  • one mutant is generated
  • 2λ mutants can be generated
  • no mutants are generated
Answer: λ mutants can be generated from one parent
Q14 | Termination condition for EA
  • mazimally allowed cpu time is elapsed
  • total number of fitness evaluations reaches a given limit
  • population diveristy drops under a given threshold
  • all the mentioned
Answer: all the mentioned
Q15 | Which of the following operator is simplest selection operator?
  • random selection
  • proportional selection
  • tournament selection
  • none
Answer: random selection
Q16 | Which crossover operators are used in evolutionary programming?
  • single point crossover
  • two point crossover
  • uniform crossover
  • evolutionary programming doesnot use crossover operators
Answer: evolutionary programming doesnot use crossover operators
Q17 | (1+1) ES
  • operates on population size of two
  • operates on populantion size of one
  • operates on populantion size of zero
  • operates on populantion size of λ
Answer: operates on population size of two
Q18 | Which of these emphasize of development of behavioral models?
  • evolutionary programming
  • genetic programming
  • genetic algorithm
  • all the mentioned
Answer: evolutionary programming
Q19 | EP applies which evolutionary operators?
  • variation through application of mutation operators
  • selection
  • both a and b
  • none of the mentioned
Answer: both a and b
Q20 | Which selection strategy works with negative fitness value?
  • roulette wheel selection
  • stochastic universal sampling
  • tournament selection
  • rank selection
Answer: rank selection
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