  More and more, optimization methods are used to find diverse solution sets.
We compare solution diversity in multi-objective optimization, multimodal
optimization, and quality diversity in a simple domain. We show that
multiobjective optimization does not always produce much diversity, multimodal
optimization produces higher fitness solutions, and quality diversity is not
sensitive to genetic neutrality and creates the most diverse set of solutions.
An autoencoder is used to discover phenotypic features automatically, producing
an even more diverse solution set with quality diversity. Finally, we make
recommendations about when to use which approach.
