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!

Check definitions in slides. Novelty is simply a search for new behaviours. Contrast this with Novelty Search with Local Competition (NSLC).

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.

A negative fitness, if naively inputed into a roulette wheel, could yield negative probabilities which does not make sense.

'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!

consider these 2 parents with a cross over point at 'x'

parent 1: 0100x100001

parent 2: 0100x111010

Child 1: 0100111010

Child 2: 0100100001

EAs do not explicitly favour diversity therefore populations often have similar characteristics. Even though noise can be injected through steps such as tournament selection, crossover and mutation, this might not be enough to push the population under the local maxima. It is important to note that while diversity schemes are aimed at overcoming local maxima, they too can get stuck!

Check slides

e.g. Novelty search vs NLSC