Intro to ML: Evolutionary Algorithms
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Assessment
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Josiah Wang
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Computers
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University
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27 plays
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Medium
Student preview
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9 questions
Show answers
1.
Multiple Choice
If we have elitism in our evolutionary algorithm, which of the following is TRUE:
The next generation completely replaces the current generation
The fitness of the population’s best individual cannot decrease
We use a mix of the current and new generations, typically using 10% of the new generation
Answer explanation
Recall, Elitism is when a small fraction (usually 10%) of the previous population is kept for the next round, ensuring that best performance of an individual in the population cannot decrease. If the kids disappoint mum or dad can step in!
2.
Multiple Select
Which of the following statements are true about Novelty Search:
Novelty Search requires a fitness function
Novelty Search requires a behavioural descriptor
The novelty archive stores all the encountered types of solutions
The novelty archive keeps only the best solution found.
Answer explanation
Check definitions in slides. Novelty is simply a search for new behaviours. Contrast this with Novelty Search with Local Competition (NSLC).
3.
Multiple Choice
Which of the following statements about Selection Operators is FALSE?
For each selection:
With the roulette wheel: an individual with twice the fitness of another individual is twice as likely to be selected
With a simple tournament: The individual with the most fitness will always be selected
With a simple tournament: The individual with the least fitness will never be selected
Answer explanation
Simple tournament selection operators choose random pairs of individuals and pick the best individual from this randomly selected group. The best performing individual maybe never get selected and you need to be at least the second worst in the population to hope to be the best in the randomly selected pair.
4.
Multiple Choice
How can you deal with a fitness function that may take negative values in a roulette wheel selection operator:
It is not possible to use the roulette wheel in these cases
We can scale the fitness values between 0 and 1 and then use the roulette wheel as per usual
Use the roulette wheel as per usual
Answer explanation
A negative fitness, if naively inputed into a roulette wheel, could yield negative probabilities which does not make sense.
5.
Multiple Choice
Which of the following would be the best fitness function for a robot trying to drive a car without crashing it:
The number of crashes while driving
Miles driven without crashes
Time spent in the car
Answer explanation
'Time spent in car' would allow for collapse, where the trivial solution of not moving anywhere would be favourable. And I hope you can realise that 'number of crashes per mile' is not something we want to maximise - remember we maximise the fitness function in the Evo setting!
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