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Artificial Intelligence - Genetic Algorithms Online Exam Quiz

Important questions about Artificial Intelligence - Genetic Algorithms. Artificial Intelligence - Genetic Algorithms MCQ questions with answers. Artificial Intelligence - Genetic Algorithms exam questions and answers for students and interviews.

1. Genetic algorithms involve which of the following phenomena ?

Options

A : Mutation

B : Cross over

C : Selection

D : All of the above

2. Which of the following is not true about fitness functions?

Options

A : They perform similar role to an objective function

B : Maximization of sum of squared residuals is an example of fitness function

C : They help in optimization

D : All of the above

3. The accuracy of results obtained from Simulated Annealing depends on:

Options

A : Temperature schedule

B : Randomness of the search

C : Initial conditions

D : All of the above

4. Simulated annealing is based on the idea that all moves that minimize cost are accepted along with some moves with low probability that increase cost. Which of the following statements is/are TRUE?

Options

A : Convergence depends on initial conditions

B : Convergence doesn’t depend on initial conditions

C : This method helps escape local minima

D : Always converges at global minima

5. Parameter space to be searched

Options

A : Chromosomes

B : Population

C : Generation

D : Colony

6. Large changes in the parameter vector independent of other parameter vectors

Options

A : Mutation

B : Crossover

C : Selection

D : Macro-mutation

7. Large changes in the parameter vector dependent on other parameter vectors

Options

A : Mutation

B : Crossover

C : Selection

D : Macro-mutation

8. Small changes in the parameter vector independent of other parameter vectors

Options

A : Mutation

B : Crossover

C : Selection

D : Macro-mutation

9. Which one of the following is the migration topology for the archipelago “archi” in the code below (Hint: Look at PyGMO documentation)

Options

A : None

B : topology.ring()

C : topology.fully_connected()

D : migration.unconnected()

10. Which of the following statements is/are true about ‘Traveling Salesman problem’ (TSP)?

Options

A : It is an NP-hard problem in combinatorial optimisation

B : There aren’t any exact algorithms known to solve TSP therefore we use heuristic techniques.

C : Ant colony optimisation can generate “good solutions” to TSP using a simulation of an ant colony

D : All of the above

11. Genetic Algorithms are

Options

A : a class of algorithms that try and build solutions by introducing evolution and selection of the best in a population of candidate solutions

B : Methods, based on the theory of natural selection and evolutionary biology, for solving optimisation problems.

C : methods for genetically modifying ants to do ant colony optimisation

D : a heuristic search method used in artificial intelligence and computing.

12. The Fitness Function in Genetic Algorithms is

Options

A : method to measure how fit a candidate solution is in solving the problem.

B : the objective function for the optimization problem being solved.

C : a substitute to approximate the survival abilities of individuals in nature.

D : a least squares approximation for a polynomial.

13. The basic idea behind Genetic Algorithms is to work with a population

Options

A : of problem solvers that interact with each other through signs.

B : of candidate solutions to try and create better candidates by mixing genes.

C : of candidate solutions in which each candidate is heuristically refined.

D : of problem solvers each of which does an independent heuristic search.

14. Which chemical is released by ants to keep track of their path?

Options

A : Deoxyribonucleic acid (DNA)

B : Pheromone

C : H 2 0

D : Citric acid

15. The basic idea behind Ant Colony Optimization algorithms is to work with a population

Options

A : of problem solvers that interact with each other through signs

B : of candidate solutions to try and create better candidates by mixing genes.

C : of candidate solutions in which each candidate is heuristically refined.

D : of problem solvers each of which does an independent heuristic search.

16. A genetic algorithm (GA) for optimization is most likely to succeed given

Options

A : a small population of fit and similar individuals.

B : a large population of fit and similar individuals.

C : a small diverse population of fit individuals.

D : a large diverse population of fit individuals.

17. Which of the following is/are True?

Options

A : The Path Representation of the TSP candidates does not allow all permutations of the cities as candidate tours, while the Adjacency Representation does.

B : In Adjacency Representation every tour has many different representations

C : In Path Representation every tour has many different representations.

D : The Adjacency Representation of the TSP candidates does not allow all permutations of the cities as candidate tours, while the Path Representation does.

18. An ant in Ant Colony Optimization algorithm for TSP produces a tour by

Options

A : a deterministic greedy constructive method.

B : a stochastic greedy constructive method.

C : a deterministic perturbation of the previous tour.

D : a stochastic perturbation of the previous tour.

19. What is the relation between the pheromone deposited by an ant on an edge and the cost of the tour generated by that ant in the ACO algorithm?

Options

A : The pheromone deposited on each edge is directly proportional to the cost of the tour.

B : The pheromone deposited on each edge is inversely proportional to the cost of the tour.

C : The pheromone deposited on each edge is constant.

D : The pheromone deposited on each edge depends upon the length of that edge.

20. What are the offspring tours generated by Order Crossover (OX) between P1 and P2 given above when the cuts are made after the 2nd and 5th cities?

Options

A : (Type: String) 56412378,23615487

B : (Type: String) 23615487,56412378

C : (Type: String) 23614487,56412372

D : (Type: String) 23319487,56415372

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