CE 602: Quiz # 2

Which of the following is a primary characteristic of binary-coded genetic algorithms?
A. Chromosomes are represented as a sequence of real numbers
B. Chromosomes are represented as binary strings
C. Mutation is not applicable
D. Fitness is calculated based on continuous variables
In a real-coded genetic algorithm, the chromosome representation consists of:
A. Binary strings
B. Integers only
C. Real numbers
D. Categorical values
Which of the following is an advantage of using real-coded genetic algorithms over binary-coded genetic algorithms?
A. Better precision in representing variables
B. Easier crossover operation
C. Faster encoding and decoding
D. All of the above
In binary-coded genetic algorithms, which of the following operations directly affects the bit representation of solutions?
A. Selection
B. Mutation
C. Fitness evaluation
D. Objective scaling
A common drawback of binary-coded genetic algorithms when dealing with high-precision problems is:
A. High memory consumption
B. Slow convergence speed
C. Limited accuracy due to binary encoding
D. Ineffective crossover operations
In Particle Swarm Optimization (PSO), each particle adjusts its position in the search space based on:
A. Its own previous best position and the previous best positions of its neighbours
B. Randomly selected values only
C. Crossover and mutation operations
D. Fixed positions in the search space
In PSO, the term "inertia weight" influences:
A. The attraction between particles
B. The probability of mutation in particles
C. The balance between exploration and exploitation in the search process
D. The size of the swarm
In Evolutionary Strategies (ES), mutation is primarily applied by:
A. Bit-flipping on binary chromosomes
B. Recombining parent solutions
C. Adding a normally distributed random value to real-valued parameters
D. Selecting the best individuals based on fitness
Which of the following selection schemes is commonly used in Evolutionary Strategies?
A. Roulette wheel selection
B. Elitism only
C. (μ + λ) selection and (μ, λ) selection
D. Tournament selection
In Differential Evolution, new candidate solutions are generated by:
A. Combining three randomly selected vectors with the current solution
B. Applying binary crossover and mutation operators
C. Selecting the best individuals only for reproduction
D. Rotating and scaling the search space
In Differential Evolution, the "mutation factor" or "scaling factor" typically:
A. Controls the rate at which solutions are selected for crossover
B. Determines the weight applied to the difference between two vectors in mutation
C. Defines the probability of selecting an individual as a parent
D. Scales the objective function's values
In NSGA-II, solutions are ranked based on:
A. Their distance from the origin
B. Their fitness value only
C. Non-dominated sorting and crowding distance
D. Their similarity to the initial population
The primary purpose of "crowding distance" in NSGA-II is to:
A. Encourage solutions to converge to the global optimum
B. Maintain diversity in the population along the Pareto front
C. Reduce the computational complexity of the algorithm
D. Ensure that only the best solution is selected
In Linear Programming, the objective function is:
A. A set of inequalities that define constraints
B. A linear equation to be maximized or minimized
C. A quadratic function
D. A nonlinear equation
Which of the following methods is commonly used to solve linear programming problems?
A. Newton-Raphson method
B. Simulated Annealing
C. Simplex method
D. Genetic Algorithm
In linear programming, a feasible solution is:
A. Any solution that satisfies all constraints
B. The optimal solution to the objective function
C. A solution that violates at least one constraint
D. Any point within the constraint boundaries
The Lagrange multiplier method is primarily used to:
A. Solve optimization problems with linear constraints
B. Solve optimization problems with non-linear constraints
C. Solve problems involving dynamic systems
D. Compute the inverse of a matrix
In the KKT conditions for a constrained optimization problem, which of the following must hold for a feasible solution to be optimal?
A. The gradient of the objective function must be zero
B. The objective function must be maximized
C. The Lagrange multipliers must be non-negative
D. The objective function and constraints must be linear
Which of the following is true about the Lagrange multipliers in the context of optimization with equality constraints?
A. They represent the sensitivity of the objective function to a constraint
B. They are always positive
C. They determine the magnitude of the objective function
D. They eliminate the need for constraints in the optimization problem
For a constrained optimization problem with inequality constraints, the KKT conditions ensure:
A. The constraints are satisfied as equalities
B. The Lagrange multipliers associated with the inequality constraints are zero when the constraints are not active
C. The solution is only feasible if all constraints are active
D. The solution is optimal if no constraints are violated