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.
@article{arxiv.2105.04252,
title = {An Analysis of Phenotypic Diversity in Multi-Solution Optimization},
author = {Alexander Hagg and Mike Preuss and Alexander Asteroth and Thomas Bäck},
journal= {arXiv preprint arXiv:2105.04252},
year = {2021}
}