Evolutionary Computation MCQs With Answers

Computer MCQs 360April 15, 2024

0 34 1 minute read

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Evolutionary Computation Online Quiz

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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

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1 / 40

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

Binary-coded GA

Regression analysis

Autoencoder

2 / 40

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

Neural network

Decision tree

Memetic algorithm

3 / 40

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

Regularization

Clustering algorithm

Convergence criteria

4 / 40

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

Apriori algorithm

K-means clustering

Differential Evolution

5 / 40

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

Natural language processing

Optimization

Image classification

6 / 40

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

Data visualization

Image segmentation

Robot navigation

7 / 40

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

Improve fitness values

Maintain population diversity

Optimize convergence

8 / 40

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

Regularization

Multi-objective optimization

Feature selection

9 / 40

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

Evaluate fitness

Select parent solutions

Introduce variability

10 / 40

What is the purpose of initialization in evolutionary algorithms?

Create initial population

Optimize convergence

Evaluate fitness

11 / 40

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

Diversity

Regularization

Gradient boosting

12 / 40

________ is a technique to handle constraints in evolutionary optimization.

Penalty function

Batch normalization

Data augmentation

13 / 40

What is Evolutionary Computation?

Data encryption

Optimization technique

Image recognition

14 / 40

What is the role of mutation operators in genetic algorithms?

Introduce new genetic material

Select parent solutions

Evaluate fitness

15 / 40

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

Apriori algorithm

Decision tree

Genetic Programming

16 / 40

What is the objective of parameter tuning in evolutionary algorithms?

Feature selection

Normalize data

Optimize algorithm performance

17 / 40

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

Random selection

Fitness proportionate selection

Tournament selection

18 / 40

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

Principal component analysis

Evolutionary strategy

Decision tree

19 / 40

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

Gradient descent

Principal component analysis

Niching

20 / 40

What is the purpose of niching in evolutionary algorithms?

Select parent solutions

Evaluate fitness

Maintain diverse solutions

21 / 40

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

Decision tree

K-nearest neighbors

Particle Swarm Optimization

22 / 40

What is the objective of hybrid algorithms in evolutionary computation?

Combine strengths of different techniques

Normalize data

Data augmentation

23 / 40

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

Evolutionary computation

Reinforcement learning

Data mining

24 / 40

What is the role of crossover operators in genetic algorithms?

Evaluate fitness

Select parent solutions

Combine genetic material

25 / 40

What is the primary disadvantage of genetic algorithms?

High computational cost

Limited to discrete variables

Premature convergence

26 / 40

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

Regularization

Empirical rule

Data augmentation

27 / 40

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

Clustering algorithm

Exploration

Gradient boosting

28 / 40

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

Mutation rate

Crossover rate

Fitness function

29 / 40

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

Regularization

Feature extraction

Natural selection

30 / 40

________ is a classic evolutionary algorithm.

K-means clustering

Genetic Algorithm (GA)

Support Vector Machine

31 / 40

What is the main advantage of evolutionary algorithms in optimization?

Handle complex search spaces

Real-time processing

Interpretability

32 / 40

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

Regularization

Selection pressure

Dimensionality reduction

33 / 40

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

Multi-armed bandit

Crossover operator

Reinforcement learning

34 / 40

What is the goal of crossover in genetic algorithms?

Evaluate fitness

Exchange genetic material

Select parent solutions

35 / 40

What is the role of elitism in evolutionary algorithms?

Mutate population

Crossover solutions

Preserve top solutions

36 / 40

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

Selection pressure

Fitness function

Initialization

37 / 40

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

Selection pressure

Population size

Fitness function

38 / 40

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

Regression analysis

Dimensionality reduction

Pareto optimization

39 / 40

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

Data mining

Estimation of Distribution Algorithm

Decision tree

40 / 40

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

Reproduction

Mutation

Initialization

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Computer MCQs 360April 15, 2024

0 34 1 minute read