An Analysis of Phenotypic Diversity in Multi-Solution Optimization
This work provides practical guidance for selecting optimization methods to achieve diverse solution sets, though it is incremental as it compares existing approaches in a limited domain.
The paper compared solution diversity across multi-objective, multimodal, and quality diversity optimization methods in a simple domain, finding that quality diversity produced the most diverse solutions, especially when enhanced with an autoencoder for automatic feature discovery.
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.