Question 1

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

Evolutionary Solver requires the user to specify parameters such as mutation rate and population size, which can significantly affect the outcome.

Question 2

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 3

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

Accepted Answer

Crossovers are a common genetic operator in genetic algorithms, where the genetic material from two or more parents is combined to generate new offspring chromosomes for the next generation.

Question 4

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

Accepted Answer

The number of clusters is often not predetermined but rather determined through exploratory analysis of the data. There are various methods for determining the ideal number of clusters, such as examining the elbow plot or using clustering algorithms that automatically determine the optimal number of clusters.

Question 5

Combinatorial problems have only a finite number of feasible solutions.

Accepted Answer

Combinatorial problems involve counting or listing all possible combinations or arrangements of a given set of elements, which is always finite.

Question 6

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 7

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

Accepted Answer

The answer of If you let a genetic algorithm run...

Question 8

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

Accepted Answer

Discriminant analysis is a statistical method used to identify a linear combination of variables that separates two or more groups. The data used in this analysis is typically referred to as a training sample. This training sample is used to develop the classification algorithm, which can then be applied to new data to predict which group each observation belongs to. Therefore, the correct choice is C, training sample. A group, cluster, or population are not the data used specifically in discriminant analysis.

Question 9

Which of the following is not a form of non-linear pricing?
A)Two-part tariff
B)Surplus value
C)Selling only single item packs 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 form of non-linear pricing. It refers to the difference between the value created by a product or service and the cost of producing it, and is not directly related to the pricing strategy used. The other options listed (two-part tariff, selling only single item packs or packs of n items, charging one price for the first n items and another price for the rest) are all examples of non-linear pricing, as they involve varying the price based on different factors such as quantity purchased or fixed fees.

Question 10

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

Accepted Answer

Evolutionary Solver works best when constraints are applied as penalties because it allows the optimization algorithm to consider violating a constraint as a last resort. Penalizing the objective function for violating constraints encourages the algorithm to find a feasible solution while still optimizing the objective function. Shortages and capacities are specific types of constraints that can be modeled as penalties. Targets, on the other hand, are not constraints and are typically set as desired values for the objective function.

Question 11

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

The number of possible paths in a shortest path problem with n locations is n!, because each location (except the starting point) can be visited in any order, leading to n! (n factorial) different permutations or paths.

Question 12

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 13

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 statement accurately describes what a discriminant score is and how it is used.

Question 14

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 one population does not necessarily guarantee good classification in other populations. The discriminant analysis model may have overfit to the specific characteristics of the sample population, leading to poor classification in other populations. It is important to validate the model on independent datasets to assess its generalizability.

Question 15

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 determines the probability of each gene being randomly changed during the evolution process. By increasing the mutation rate, the algorithm is more likely to explore a larger area of the feasible region, even if it means moving away from the current best solution. This can help prevent the algorithm from getting stuck in a local optimum and instead find the global optimum. Increasing the population size can also help with exploration, but it can also slow down the convergence of the algorithm. Random seed does not affect the exploration of the feasible region.

Question 16

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 17

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

Accepted Answer

Mutations introduce new genetic material into the population, which can help the algorithm explore new areas of the search space and potentially find better solutions if it was stuck in a local optimum.

Question 18

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 the traveling salesman problem with 11 cities is actually (11-1)! = 10! = 3,628,800.

Question 19

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

Accepted Answer

The fitness function is the objective function in a genetic algorithm, as it determines the fitness or suitability of a particular individual in the population for selection and crossover. Chromosomes, populations, and generations are all components of the genetic algorithm framework, but do not represent the objective function.

Question 20

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 is a basic arithmetic function that does not require the use of Evolutionary Solver to obtain solutions. The other functions listed (IF, MAX, MIN) may require optimization or finding the best solution, which can be achieved using Evolutionary Solver.

Quiz 8: Evolutionary Solver: An Alternative Optimization Procedure

Which of the following is true regarding Evolutionary Solver?

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

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

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

Combinatorial problems have only a finite number of feasible solutions.

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

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

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

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

Evolutionary Solver works best when constraints are applied as:

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

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

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.

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.

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

Evolutionary Solver is primarily used to solve models where:

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

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

The objective function in a genetic algorithm is called a:

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