Evolutionary Computation MCQs With Answers

Computer MCQs 360April 15, 2024

0 24 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

What is the purpose of initialization in evolutionary algorithms?

Evaluate fitness

Create initial population

Optimize convergence

2 / 40

What is the purpose of niching in evolutionary algorithms?

Evaluate fitness

Maintain diverse solutions

Select parent solutions

3 / 40

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

Clustering algorithm

Gradient boosting

Exploration

4 / 40

________ is a classic evolutionary algorithm.

Support Vector Machine

Genetic Algorithm (GA)

K-means clustering

5 / 40

What is the role of elitism in evolutionary algorithms?

Preserve top solutions

Crossover solutions

Mutate population

6 / 40

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

Fitness proportionate selection

Random selection

Tournament selection

7 / 40

What is the objective of hybrid algorithms in evolutionary computation?

Combine strengths of different techniques

Normalize data

Data augmentation

8 / 40

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

Fitness function

Mutation rate

Crossover rate

9 / 40

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

Dimensionality reduction

Selection pressure

Regularization

10 / 40

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

Estimation of Distribution Algorithm

Decision tree

Data mining

11 / 40

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

Robot navigation

Data visualization

Image segmentation

12 / 40

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

Particle Swarm Optimization

K-nearest neighbors

Decision tree

13 / 40

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

Data augmentation

Empirical rule

Regularization

14 / 40

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

Pareto optimization

Regression analysis

Dimensionality reduction

15 / 40

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

Fitness function

Selection pressure

Population size

16 / 40

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

Niching

Gradient descent

Principal component analysis

17 / 40

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

Autoencoder

Binary-coded GA

Regression analysis

18 / 40

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

Natural language processing

Image classification

Optimization

19 / 40

What is the role of crossover operators in genetic algorithms?

Evaluate fitness

Select parent solutions

Combine genetic material

20 / 40

What is the primary disadvantage of genetic algorithms?

Limited to discrete variables

High computational cost

Premature convergence

21 / 40

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

Regularization

Diversity

Gradient boosting

22 / 40

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

Memetic algorithm

Decision tree

Neural network

23 / 40

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

Introduce variability

Select parent solutions

Evaluate fitness

24 / 40

What is Evolutionary Computation?

Optimization technique

Data encryption

Image recognition

25 / 40

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

Mutation

Reproduction

Initialization

26 / 40

What is the main advantage of evolutionary algorithms in optimization?

Real-time processing

Interpretability

Handle complex search spaces

27 / 40

________ is a technique to handle constraints in evolutionary optimization.

Penalty function

Data augmentation

Batch normalization

28 / 40

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

Multi-objective optimization

Regularization

Feature selection

29 / 40

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

Convergence criteria

Regularization

Clustering algorithm

30 / 40

What is the objective of parameter tuning in evolutionary algorithms?

Feature selection

Optimize algorithm performance

Normalize data

31 / 40

What is the role of mutation operators in genetic algorithms?

Introduce new genetic material

Evaluate fitness

Select parent solutions

32 / 40

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

Regularization

Natural selection

Feature extraction

33 / 40

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

Optimize convergence

Maintain population diversity

Improve fitness values

34 / 40

What is the goal of crossover in genetic algorithms?

Select parent solutions

Evaluate fitness

Exchange genetic material

35 / 40

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

Fitness function

Initialization

Selection pressure

36 / 40

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

Reinforcement learning

Multi-armed bandit

Crossover operator

37 / 40

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

K-means clustering

Differential Evolution

Apriori algorithm

38 / 40

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

Evolutionary strategy

Decision tree

Principal component analysis

39 / 40

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

Decision tree

Apriori algorithm

Genetic Programming

40 / 40

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

Evolutionary computation

Data mining

Reinforcement learning

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Computer MCQs 360April 15, 2024

0 24 1 minute read