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Question 1[1 Mark]
Given a significant number of cities (nodes), which of the following is not a valid reason for using a Genetic Algorithm (GA) to solve the Travelling Salesman Problem (TSP)?
(a).
Owing to the extremely large number of permutations of cities, brute force approaches are intractable.
(b).
Given the vast fitness landscape, more traditional hill-climbing approaches may get trapped at local extrema.
(c).
Calculus is generally a poor fit for fundamentally discrete mathematical problems such as the TSP.
(d).
As long as premature convergence is avoided, GAs offer an efficient method / heuristic to guarantee an optimal solution;
Question 2[1 Mark]
Given a list of cities and the distances between each pair of cities, a genetic algorithm is designed to determine the length of the/a shortest possible route that visits each city exactly once and returns to the origin city. The genetic algorithm applies a fitness function to each candidate solution such that those with the lowest total distance are attributed the highest fitness value. In terms of the fitness function and problem space (search space), the algorithm seeks to find which of the following?
(a).
local minimum
(b).
global minimum
(c).
local maximum
(d).
global maximum
Question 3[1 Mark]
Genetic operators define the critical and configurable sub procedures which guide a genetic algorithm meta-heuristic towards a solution. Which of the following is not a genetic operator?
(a).
Mutation
(b).
Crossover
(c).
Convergence
(d).
Selection
Question 4[1 Mark]
With respect to Genetic Algorithms (GAs), which of the following is not directly affected by mutation rate.
(a).
Crossover strategy
(b).
Candidate solution diversity
(c).
Problem space exploration
(d).
Premature convergence
Question 5[1 Mark]
Which of the following Genetic Algorithm (GA) actions do not typically involve randomness?
(a).
Executing Partially Mapped Crossover (PMX)
(b).
Mutating offspring
(c).
Generating an initial population
(d).
Rewarding novelty

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