Intro to ML: Evolutionary Algorithms

Intro to ML: Evolutionary Algorithms

Assessment

Assessment

Created by

Josiah Wang

Computers

University

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9 questions

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1.

Multiple Choice

1 min

1 pt

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

1 min

1 pt

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

1 min

1 pt

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

1 min

1 pt

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

1 min

1 pt

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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