Artificial Intelligence

Evolutionary Computation MCQs With Answers

Welcome to the Evolutionary Computation MCQs with Answers. In this post, we have shared Evolutionary Computation Online Test for different competitive exams. Find practice Evolutionary Computation Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Evolutionary Computation.

Evolutionary Computation Online Quiz

By presenting 3 options to choose from, Evolutionary Computation Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Evolutionary Computation Questions and Answers and click over the correct answer.

  • Test Name: Evolutionary Computation MCQ Quiz Practice
  • Type: Quiz Test
  • Total Questions: 40
  • Total Marks: 40
  • Time: 40 minutes

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Evolutionary Computation MCQs

Evolutionary Computation

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1 / 40

________ is a problem-solving technique that evolves a population of solutions over generations.

2 / 40

________ is an evolutionary algorithm variant that uses a population-based search to optimize solutions.

3 / 40

What is the objective of parameter tuning in evolutionary algorithms?

4 / 40

________ is a technique inspired by the process of natural selection.

5 / 40

________ is a technique used to balance exploration and exploitation in evolutionary computation.

6 / 40

________ is a method to optimize discrete variables in evolutionary computation.

7 / 40

________ is an algorithm used for optimizing continuous functions in evolutionary computation.

8 / 40

________ is a technique to handle multi-objective optimization problems in evolutionary computation.

9 / 40

________ is a characteristic of evolutionary algorithms that ensures exploring different regions of the solution space.

10 / 40

________ is used to evaluate the quality of solutions in evolutionary algorithms.

11 / 40

________ is an approach that combines genetic algorithms with local search methods.

12 / 40

________ is an algorithm used for optimizing combinatorial problems in evolutionary computation.

13 / 40

________ is an approach in evolutionary computation that mimics the survival of the fittest concept.

14 / 40

What is the primary goal of diversity preservation in evolutionary algorithms?

15 / 40

What is the goal of crossover in genetic algorithms?

16 / 40

________ is a classic evolutionary algorithm.

17 / 40

What is the role of mutation operators in genetic algorithms?

18 / 40

________ is an evolutionary algorithm variant that uses probabilistic models to represent and generate solutions.

19 / 40

________ is a characteristic of evolutionary algorithms that ensures selecting better solutions over generations.

20 / 40

What is the objective of hybrid algorithms in evolutionary computation?

21 / 40

________ is a parameter that controls the balance between exploration and exploitation in evolutionary algorithms.

22 / 40

________ is the process of generating offspring solutions in evolutionary algorithms.

23 / 40

________ is an application area where evolutionary computation is commonly used.

24 / 40

What is the purpose of niching in evolutionary algorithms?

25 / 40

What is the role of elitism in evolutionary algorithms?

26 / 40

________ is a technique to evaluate candidate solutions in evolutionary algorithms using multiple criteria.

27 / 40

What does the term "mutation" refer to in evolutionary algorithms?

28 / 40

________ is an evolutionary algorithm variant that focuses on maintaining diversity in the population.

29 / 40

What is Evolutionary Computation?

30 / 40

________ is a technique to handle constraints in evolutionary optimization.

31 / 40

What is the main advantage of evolutionary algorithms in optimization?

32 / 40

What is the purpose of initialization in evolutionary algorithms?

33 / 40

________ is an application area where evolutionary algorithms are used for robotics.

34 / 40

________ is a technique to determine the termination condition in evolutionary algorithms.

35 / 40

________ is a method to determine the population size in evolutionary algorithms.

36 / 40

________ is a characteristic of evolutionary algorithms that avoids premature convergence.

37 / 40

________ is a selection method that chooses solutions based on their fitness values.

38 / 40

What is the primary disadvantage of genetic algorithms?

39 / 40

What is the role of crossover operators in genetic algorithms?

40 / 40

________ is a technique to guide the search towards promising regions in the solution space.

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