CA522 quiz -1

Which of the following is NOT an optimization model?

• Linear Programming

• Integer Programming

• Metaheuristic Optimization

• Sorting Algorithms

Which of the following is commonly used as an approximate algorithm for NP-hard problems?

• Branch-and-Bound

• Genetic Algorithm

• Dynamic Programming

• Quadratic Programming

When is it best to use metaheuristics?

• When an exact method is too slow for large-scale problems

• When the problem has a closed-form solution

• When a unique optimal solution is guaranteed

• When the problem is solvable using brute force

In optimization, what does the term "fitness landscape" refer to?

• The geographical area where optimization takes place

• The space of solutions and their corresponding objective function values

• The best solution found in an optimization problem

• The memory used by an algorithm

What is the primary role of the objective function in optimization?

• To define the search space

• To evaluate the quality of a solution

• To generate the initial solutions

• To provide constraints for the search process

In constraint handling within optimization, what is a common technique?

• Ignoring constraints completely

• Using penalty functions

• Multiplying the objective function by a constant factor

• Eliminating feasible solutions

What is the key principle behind Tabu Search?

• Keeping a list of recently visited solutions to avoid cycling

• Always choosing the best neighboring solution

• Randomly jumping to new solutions

• Restarting the search every few iterations

How does Simulated Annealing escape local optima?

• By using a deterministic approach

• By allowing probabilistic acceptance of worse solutions

• By storing multiple solutions at once

• By running multiple searches in parallel

What is the main concept of Iterated Local Search (ILS)?

• Performing local search on multiple solutions in parallel

• Repeatedly restarting with random solutions

• Using local search with perturbations to escape local optima

• Using a fixed-size memory for tracking solutions

What is the main idea of Hill Climbing?

• It always accepts a better neighbor

• It uses a probabilistic mechanism to accept worse solutions

• It maintains a population of solutions

• It jumps randomly across the solution space