Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity

Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While research has focused on improving specific aspects of Quality-Diversity algorithms, surprisingly little attention has been paid to investigating alternative formulations of local competition itself — a core mechanism distinguishing Quality-Diversity from traditional evolutionary algorithms. Most current approaches implement local competition using explicit collection mechanisms like fixed grids or unstructured archives. These often rely on predefined bounds or hard-to-tune parameters, presenting opportunities for alternative strategies. We outline how Quality-Diversity methods can be framed as Genetic Algorithms where local competition occurs through fitness transformations rather than explicit collection mechanisms. Inspired by this insight, we introduce Dominated Novelty Search, a Quality-Diversity algorithm that implements local competition through dynamic fitness transformations, without relying on predefined bounds or parameters. Our experiments show that Dominated Novelty Search significantly outperforms existing approaches across standard Quality-Diversity benchmarks, while maintaining its advantage in challenging scenarios like high-dimensional or unsupervised behavior spaces.