Question 1

A traveling salesman problem with a starting city and 10 other cities that must be visited has 1,826,800 possible solutions.

Accepted Answer

The number of possible solutions for a traveling salesman problem with 11 cities (including the starting city) is actually 39,916,800. The formula to calculate the number of possible solutions for a TSP with n cities is (n-1)!/2. Therefore, for this particular problem, the formula would be (10!)/2, which equals 1,814,400 possible solutions.

Question 2

In cluster analysis,the number of clusters is usually assigned ahead of time.

Accepted Answer

The number of clusters in cluster analysis is not usually assigned ahead of time, but rather determined through various techniques such as visualization, computational methods, and evaluation of cluster quality.

Question 3

If you let a genetic algorithm run long enough,it is guaranteed to find the solution to any optimization problem.

Accepted Answer

True

Question 4

A discriminant score is a weighted combination of the data for each member of a population,which is used to classify the members into groups.

Accepted Answer

This is the correct definition of a discriminant score.

Question 5

The objective function in a genetic algorithm is called a:
A) chromosome
B) fitness function
C) population
D) generation

Accepted Answer

The objective function in a genetic algorithm is called the fitness function. It determines the fitness or suitability of the individuals in a population to serve as parents for the next generation. The chromosome represents an individual solution, the population represents the collection of individuals, and the generation represents a iteration or breeding cycle of the algorithm.

Question 6

Evolutionary Solver works best when constraints are applied as:
A) penalties
B) shortages
C) capacities
D) targets

Accepted Answer

Evolutionary Solver is designed to work with penalty functions. Constraints are applied as penalties that increase the fitness function when violated, which encourages the solver to find solutions that satisfy the constraints while optimizing the objective function. This approach is preferable to using shortages, capacities, or targets, which can make the problem more complicated and reduce the ability of the solver to find globally optimal solutions.

Question 7

If the classification matrix from a discriminant analysis shows a high percentage of correct classification,then we can be confident that the classification will work well for other populations.

Accepted Answer

A high percentage of correct classification in the classification matrix from a discriminant analysis only shows that the model works well for the particular population and data used in the analysis. It does not necessarily indicate that the classification will work well for other populations. Hence, we cannot be confident about the classification's effectiveness for other populations based on this alone.

Question 8

Which of the following is not a form of non-linear pricing?
A) Two-part tariff
B) Surplus value
C) Selling only single item or packs of n items
D) Charging one price for the first n items and another price for the rest

Accepted Answer

Surplus value is not a pricing strategy; it refers to the difference between the value produced by labor and the wages paid to labor, often discussed in economic theory.

Question 9

Which of the following is not a step in a genetic algorithm?
A) Generating a population
B) Creating a new generation
C) Culling the population
D) Evaluating a stopping condition

Accepted Answer

Culling the population is not a standard term used in the context of genetic algorithms. Genetic algorithms typically involve generating a population, creating new generations through selection and genetic operators (like crossover and mutation), and evaluating a stopping condition to determine when the algorithm should terminate. Culling, or selectively removing individuals from the population, is not a standard step; instead, selection processes determine which individuals contribute to the next generation.

Question 10

The data used to classify members in a discriminant analysis is called a:
A) group
B) cluster
C) training sample
D) population

Accepted Answer

The data used in a discriminant analysis is typically a training sample, which refers to a subset of the population used to develop and test the classification model. The other answer choices (group, cluster, population) are not specific to discriminant analysis and may refer to different types of data or analysis.

Question 11

The initial set of chromosomes generated by a genetic algorithm is called the population.

Accepted Answer

The initial set of chromosomes generated by a genetic algorithm is called the population.

Question 12

Which of the following is true regarding Evolutionary Solver?
A) It is efficient at handling constraints
B) A good starting is critical to the success of it finding a solution
C) It places more of a burden on you to specify certain parameters of the algorithm
D) Running it a second time for a problem should yield the same solution

Accepted Answer

The answer of Which of the following is true regarding...

Question 13

Which of the following is not one of the Excel functions that necessitate the use of Evolutionary Solver to obtain solutions?
A) SUM
B) IF
C) MAX
D) MIN

Accepted Answer

The SUM function does not require the use of Evolutionary Solver as it is a simple mathematical calculation that can be done using basic Excel formulas. IF, MAX, and MIN functions may require the use of Solver if they are used in complex models with multiple constraints and variables.

Question 14

The number of possible paths in a shortest path problem with n locations is: A) n B) n - 1 C) n² D) n!

Accepted Answer

In a shortest path problem with n locations, the number of possible paths is n! because you can visit each location in any order, which is a permutation of the n locations.

Question 15

Mutations are useful for getting a genetic algorithm "unstuck".

Accepted Answer

Mutations introduce new genetic diversity into the population, which can allow the algorithm to explore new areas of the solution space and potentially break through a "stuck" or stagnant point.

Question 16

In Evolutionary Solver,the initial population is chosen randomly,so the starting solution you enter in the changing cells is irrelevant.

Accepted Answer

In Evolutionary Solver, the starting solution you enter can influence the initial population and potentially guide the search process, making it relevant to the outcome.

Question 17

Evolutionary Solver is primarily used to solve models where:
A) some or all of the changing cells are restricted to be binary and/or integer
B) the objective cell and constraints are "smooth" functions of the changing cells
C) there are numerous constraints
D) the objective call and constraints are "nonsmooth" functions of the changing cells

Accepted Answer

The answer of Evolutionary Solver is primarily used to solve...

Question 18

Crossovers are rare as a genetic algorithm is generating chromosomes for the next generations.

Accepted Answer

Crossovers are a key operator in genetic algorithms for the creation of new chromosomes during reproduction.

Question 19

Combinatorial problems have only a finite number of feasible solutions.

Accepted Answer

Combinatorial problems involve counting the number of feasible solutions from a given set of options, which are often limited or finite in number. Therefore, these problems typically have a finite number of feasible solutions.

Question 20

The Evolutionary Solver setting that helps the algorithm explore a completely different area of the feasible region is:
A) convergence
B) mutation rate
C) population size
D) random seed

Accepted Answer

The mutation rate is a setting in the Evolutionary Solver which can introduce small random changes in the solution variables of each individual in the population. This can help the algorithm explore a completely different area of the feasible region that may not have been discovered otherwise. Therefore, B is the best choice for the given question.

Quiz 8: Evolutionary Solver: an Alternative Optimization Procedure

A traveling salesman problem with a starting city and 10 other cities that must be visited has 1,826,800 possible solutions.

In cluster analysis,the number of clusters is usually assigned ahead of time.

If you let a genetic algorithm run long enough,it is guaranteed to find the solution to any optimization problem.

A discriminant score is a weighted combination of the data for each member of a population,which is used to classify the members into groups.

The objective function in a genetic algorithm is called a:

Evolutionary Solver works best when constraints are applied as:

If the classification matrix from a discriminant analysis shows a high percentage of correct classification,then we can be confident that the classification will work well for other populations.

Which of the following is not a form of non-linear pricing?

Which of the following is not a step in a genetic algorithm?

The data used to classify members in a discriminant analysis is called a:

The initial set of chromosomes generated by a genetic algorithm is called the population.

Which of the following is true regarding Evolutionary Solver?

Which of the following is not one of the Excel functions that necessitate the use of Evolutionary Solver to obtain solutions?

The number of possible paths in a shortest path problem with n locations is:

Mutations are useful for getting a genetic algorithm "unstuck".

In Evolutionary Solver,the initial population is chosen randomly,so the starting solution you enter in the changing cells is irrelevant.

Evolutionary Solver is primarily used to solve models where:

Crossovers are rare as a genetic algorithm is generating chromosomes for the next generations.

Combinatorial problems have only a finite number of feasible solutions.

The Evolutionary Solver setting that helps the algorithm explore a completely different area of the feasible region is: