Evolutionary Algorithms MCQs With Answers

Computer MCQs 360April 16, 2024

0 9 1 minute read

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

Evolutionary Algorithms Online Quiz

By presenting 3 options to choose from, Evolutionary Algorithms 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 Algorithms Questions and Answers and click over the correct answer.

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

Note: Answer of the questions will change randomly each time you start the test. Practice each quiz test at least 3 times if you want to secure High Marks. Once you are finished, click the View Results button. If any answer looks wrong to you in Quizzes. simply click on question and comment below that question. so that we can update the answer in the quiz section.

Evolutionary Algorithms

Please fill out the form before starting Quiz.

1 / 40

Which evolutionary algorithm concept is inspired by natural selection?

Particle Swarm Optimization

Genetic algorithms

Simulated Annealing

2 / 40

What is the primary goal of differential evolution algorithms?

Combine genetic material

Optimize continuous functions

Solve discrete problems

3 / 40

Which evolutionary algorithm is inspired by the social behavior of birds and fish?

Particle Swarm Optimization

Simulated Annealing

Genetic algorithms

4 / 40

Which algorithm is inspired by the foraging behavior of ants?

Ant Colony Optimization

Particle Swarm Optimization

Genetic algorithms

5 / 40

Which evolutionary algorithm concept involves evaluating the entire population at once?

Steady state model

Generational model

Island model

6 / 40

What is the purpose of a fitness proportionate selection in genetic algorithms?

Alter genes

Combine genetic material

Select individuals based on fitness

7 / 40

Which term describes the process of selecting the best individuals to reproduce in genetic algorithms?

Crossover

Mutation

Selection

8 / 40

Which evolutionary algorithm is based on the principles of Darwinian evolution?

Genetic algorithms

Ant Colony Optimization

Particle Swarm Optimization

9 / 40

Which term refers to a potential solution in a genetic algorithm?

Population

Gene

Chromosome

10 / 40

Which term describes the complete set of solutions being considered in an evolutionary algorithm?

Population

Chromosome

Gene

11 / 40

Which operator in genetic algorithms introduces diversity by randomly altering genes?

Mutation

Crossover

Selection

12 / 40

Which operator in genetic algorithms helps to prevent the population from becoming too similar?

Selection

Mutation

Crossover

13 / 40

Which term refers to the adjustment of gene values within a chromosome in genetic algorithms?

Crossover

Mutation

Selection

14 / 40

What is the main advantage of using evolutionary algorithms?

Fast convergence

Guaranteed optimal solutions

Adaptability

15 / 40

What is the main benefit of using tournament selection in genetic algorithms?

Maintains diversity

Guarantees optimal solution

Simplicity

16 / 40

Which term refers to a single variable within a chromosome in genetic algorithms?

Population

Chromosome

Gene

17 / 40

Which operator in genetic algorithms is responsible for creating new offspring?

Selection

Mutation

Crossover

18 / 40

What is the primary goal of evolutionary strategies in optimization problems?

Combine genetic material

Adapt and evolve solutions

Preserve best individuals

19 / 40

What is the main purpose of the recombination operator in genetic algorithms?

Evaluate fitness

Select parents

Create offspring

20 / 40

What is the main characteristic of a generational model in genetic algorithms?

Migration between subpopulations

Individuals replaced one at a time

Entire population replaced at once

21 / 40

What is the purpose of the roulette wheel selection method in genetic algorithms?

Alter genes

Select individuals based on fitness

Combine genetic material

22 / 40

What is the main goal of selection in genetic algorithms?

Choose the fittest individuals

Combine genetic material

Alter genes

23 / 40

What is the main characteristic of evolutionary programming?

Uses deterministic selection

Simulates evolution without crossover

Focuses on recombination

24 / 40

Which evolutionary algorithm is inspired by the behavior of swarms?

Genetic algorithms

Particle Swarm Optimization

Ant Colony Optimization

25 / 40

What is the main purpose of crossover in genetic algorithms?

Combine genetic material

Select parents

Mutate genes

26 / 40

What is the key difference between genetic algorithms and evolutionary strategies?

Representation of solutions

Fitness evaluation

Use of crossover

27 / 40

What is the primary goal of the fitness function in evolutionary algorithms?

Combine genetic material

Alter genes

Evaluate solution quality

28 / 40

What is the primary use of fitness scaling in genetic algorithms?

Alter genes

Prevent premature convergence

Combine genetic material

29 / 40

What is a fitness function in the context of evolutionary algorithms?

Alters genes

Evaluates solution quality

Combines genetic material

30 / 40

What is the main purpose of the fitness landscape in evolutionary algorithms?

Alter genes

Combine genetic material

Visualize solution quality

31 / 40

Which evolutionary algorithm concept involves individuals migrating between subpopulations?

Island model

Elitism

Steady state model

32 / 40

Which evolutionary algorithm concept involves individuals being replaced one at a time?

Steady state model

Island model

Generational model

33 / 40

What is the main purpose of the mutation operator in genetic algorithms?

Introduce diversity

Select parents

Evaluate fitness

34 / 40

What is the role of elitism in genetic algorithms?

Introduce diversity

Combine genetic material

Preserve best individuals

35 / 40

Which evolutionary algorithm concept is inspired by the behavior of social insects?

Particle Swarm Optimization

Ant Colony Optimization

Genetic algorithms

36 / 40

Which evolutionary algorithm is particularly useful for optimizing real-valued functions?

Genetic algorithms

Differential Evolution

Ant Colony Optimization

37 / 40

Which term describes the process of combining two parent chromosomes to create offspring?

Mutation

Crossover

Selection

38 / 40

What is the main advantage of using particle swarm optimization?

Simplicity and efficiency

Fast mutation rates

Guaranteed optimal solutions

39 / 40

Which evolutionary algorithm is inspired by the annealing process in metallurgy?

Particle Swarm Optimization

Genetic algorithms

Simulated Annealing

40 / 40

What is the key advantage of using elitism in genetic algorithms?

Combine genetic material

Introduce diversity

Preserve best solutions

Download Certificate of Quiz Evolutionary Algorithms

On the end of Quiz, you can download the certificate of the quiz if you got more than 70% marks. Add a certificate to your job application or social profile (like LinkedIn) and get more job offers.

Download Evolutionary Algorithms MCQs with Answers Free PDF

You can also download 100 Evolutionary Algorithms Questions with Answers free PDF from the link provided below. To Download file in PDF click on the arrow sign at the top right corner.

If you are interested to enhance your knowledge regarding English, Physics, Chemistry, and Biology please click on the link of each category, you will be redirected to dedicated website for each category.

Computer MCQs 360April 16, 2024

0 9 1 minute read