Evolutionary Computation MCQs With Answers

Computer MCQs 360April 15, 2024

0 43 1 minute read

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

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

Crossover rate

Fitness function

Mutation rate

2 / 40

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

Data mining

Estimation of Distribution Algorithm

Decision tree

3 / 40

What is the purpose of niching in evolutionary algorithms?

Select parent solutions

Maintain diverse solutions

Evaluate fitness

4 / 40

What is the objective of hybrid algorithms in evolutionary computation?

Normalize data

Data augmentation

Combine strengths of different techniques

5 / 40

________ is a technique to handle constraints in evolutionary optimization.

Batch normalization

Data augmentation

Penalty function

6 / 40

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

Decision tree

Memetic algorithm

Neural network

7 / 40

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

Maintain population diversity

Optimize convergence

Improve fitness values

8 / 40

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

Decision tree

Principal component analysis

Evolutionary strategy

9 / 40

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

Gradient boosting

Diversity

Regularization

10 / 40

What is the role of crossover operators in genetic algorithms?

Combine genetic material

Evaluate fitness

Select parent solutions

11 / 40

What is the main advantage of evolutionary algorithms in optimization?

Handle complex search spaces

Real-time processing

Interpretability

12 / 40

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

Selection pressure

Dimensionality reduction

Regularization

13 / 40

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

Regression analysis

Binary-coded GA

Autoencoder

14 / 40

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

Crossover operator

Reinforcement learning

Multi-armed bandit

15 / 40

What is Evolutionary Computation?

Optimization technique

Data encryption

Image recognition

16 / 40

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

Regularization

Natural selection

Feature extraction

17 / 40

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

Reproduction

Mutation

Initialization

18 / 40

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

Data visualization

Image segmentation

Robot navigation

19 / 40

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

Population size

Fitness function

Selection pressure

20 / 40

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

Exploration

Gradient boosting

Clustering algorithm

21 / 40

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

Regression analysis

Pareto optimization

Dimensionality reduction

22 / 40

What is the role of mutation operators in genetic algorithms?

Evaluate fitness

Select parent solutions

Introduce new genetic material

23 / 40

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

Feature selection

Multi-objective optimization

Regularization

24 / 40

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

Introduce variability

Evaluate fitness

Select parent solutions

25 / 40

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

Tournament selection

Random selection

Fitness proportionate selection

26 / 40

What is the primary disadvantage of genetic algorithms?

High computational cost

Premature convergence

Limited to discrete variables

27 / 40

What is the objective of parameter tuning in evolutionary algorithms?

Feature selection

Normalize data

Optimize algorithm performance

28 / 40

________ is a classic evolutionary algorithm.

Genetic Algorithm (GA)

Support Vector Machine

K-means clustering

29 / 40

What is the goal of crossover in genetic algorithms?

Evaluate fitness

Exchange genetic material

Select parent solutions

30 / 40

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

Image classification

Natural language processing

Optimization

31 / 40

What is the role of elitism in evolutionary algorithms?

Preserve top solutions

Crossover solutions

Mutate population

32 / 40

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

Data augmentation

Empirical rule

Regularization

33 / 40

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

Regularization

Convergence criteria

Clustering algorithm

34 / 40

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

Decision tree

K-nearest neighbors

Particle Swarm Optimization

35 / 40

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

Differential Evolution

K-means clustering

Apriori algorithm

36 / 40

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

Niching

Principal component analysis

Gradient descent

37 / 40

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

Reinforcement learning

Data mining

Evolutionary computation

38 / 40

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

Decision tree

Apriori algorithm

Genetic Programming

39 / 40

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

Fitness function

Selection pressure

Initialization

40 / 40

What is the purpose of initialization in evolutionary algorithms?

Evaluate fitness

Optimize convergence

Create initial population

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Computer MCQs 360April 15, 2024

0 43 1 minute read