Saga Quiz

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GA Questions

1. All representations of the objective function need to be written in the binary '1/0' format.
   1. True
   2. False
   
   
2. The only operators available in creating a new generation are Crossover, Mirror, and Mutation.
   1. True
   2. False
   
   
3. If there is only one strain in the current population, we can introduce new strains in the next generation by using Crossover as the only gene operator.
   1. True
   2. False
   
   
4. The assignment of 60% Crossover, 30% Mirror, and 10% Mutation is considered a good distribution of gene operators.
   1. True
   2. False
   
   
5. Using a Genetic Algorithm guarantees you will find a very good solution in a reasonable amount of time.
   1. True
   2. False
   
   
6. A Genetic Algorithm will work best if there are a variety of strains in the initial population.
   1. True
   2. False
   
   
7. It is always better to use large populations/fewer generations than small populations/more generations when searching for the best solution.
   1. True
   2. False
   
   
8. Once a strain disappears from the population it can never reappear in the population again.
   1. True
   2. False
   
   
9. A Genetic Algorithm will find the optimal solution given the correct mix of operators and enough time.
   1. True
   2. False
   
   
10. The same solution to a problem may be arrived at in a different number of generations.
    1. True
    2. False
    
    
11. Mutating a strain is:
    1. Changing all the genes in the strain.
    2. Removing one gene in the strain.
    3. Randomly changing one gene in the strain.
    4. Removing the strain from the population.
    
    
12. Genetic Algorithms are considered pseudo-random because they:
    1. Search the solution space in a random fashion.
    2. Search the solution space using the previous generation as a starting point.
    3. Have no knowledge of what strains are contained in the next generation.
    4. Use random numbers.
    
    
13. The three gene operators we have discussed can be thought of as:
    1. Crossover: Receiving the best genes from both parents.
    2. Mutation: Changing one gene so that the child is almost like the parent.
    3. Mirror: Changing a string of genes in the child so it is like a  cousin to the parent.
    4. A and B only
    5. All of the above
    
    
14. If a population contains only one strain, you can introduce new strains by:
    1. Using the Crossover operator.
    2. Injecting random strains into the population.
    3. Using the Mutation operator.
    4. B only
    5. B and C only
    
    
15. The efficiency of a Genetic Algorithm (how quickly it arrives at the best solution) is dependent upon:
    1. The initial conditions.
    2. The size of the population.
    3. The types of operators employed.
    4. All of the above
    
    
16. Which of the following differences between Simulated Annealing and Genetic Algorithm are fundamental?
    1. There is an implicit parallelism in GA, none in SA.
    2. In SA, each solution is generated from the current solution (move right or left in the Bayser SA tool). In GA, a solution can be generated by crossing two parents (crossover operation).
    3. In SA, a candidate solution always lies in the vicinity of the current solution. In GA, the candidate solutions may be far from the current solutions.
    4. All of the above.
    
    
17. If two successive generations are identical then the Genetic Algorithm has found the optimal solution. Discuss why this statement is NOT true.

18. Are Genetic Algorithms useful if we don t have a full understanding of our objective function?

19. Genetic Algorithms are predicated on the fact that the path to a good solution is based on having good intermediary solutions. How will the Genetic Algorithm act if there are discontinuities in the solution space?

20. Say we want to model the behavior of a foxes-and-hares eco-system. How would you extend the GA model to capture the equilibrium among different strains, i.e., the food chain equilibrium between foxes and hares?

21. How might one use Genetic Algorithms in forecasting models?

22. Link the idea of temperature in Simulated Annealing to the intensity (number of genes) and frequency (how often) of mutation in Genetic Algorithms.

23. What are the pros and cons of SA and GA? Can you conceive of a framework that combines the best of both worlds.