# Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity

arXiv: https://arxiv.org/abs/2502.00593 · HTML: https://mlsanity.com/paper/2502.00593/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm](https://mlsanity.com/paper/1610.05729/) (2016)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Unsupervised Behaviour Discovery with Quality-Diversity Optimisation](https://mlsanity.com/paper/2106.05648/) (2021)
- [Multi-Objective Quality Diversity Optimization](https://mlsanity.com/paper/2202.03057/) (2022)
- [QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration](https://mlsanity.com/paper/2308.03665/) (2023)
- [Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains](https://mlsanity.com/paper/2302.00463/) (2023)
- [Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2401.08632/) (2023)
- [Discovering Evolutionary Stepping Stones through Behavior Domination](https://mlsanity.com/paper/1704.05554/) (2017)

## Built on by (most cited first, 8 of 8)

- [Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization](https://mlsanity.com/paper/2502.02190/) (2025)
- [Soft Quality-Diversity Optimization](https://mlsanity.com/paper/2512.00810/) (2025)
- [Interactive LLM-assisted Curriculum Learning for Multi-Task Evolutionary Policy Search](https://mlsanity.com/paper/2602.10891/) (2026)
- [Quality-Diversity Optimization as Multi-Objective Optimization](https://mlsanity.com/paper/2602.00478/) (2026)
- [Distributional Value Estimation Without Target Networks for Robust Quality-Diversity](https://mlsanity.com/paper/2604.20381/) (2026)
- [GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data](https://mlsanity.com/paper/2609.08719/) (2026)
- [Continual Reinforcement Learning with Neuroevolution](https://mlsanity.com/paper/2610.01583/) (2026)
- [Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework](https://mlsanity.com/paper/2610.15724/) (2026)

## Abstract (arXiv)

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 -- the core mechanism distinguishing Quality-Diversity from traditional evolutionary algorithms. Most approaches implement local competition through explicit collection mechanisms like fixed grids or unstructured archives, imposing artificial constraints that require predefined bounds or hard-to-tune parameters. We show that Quality-Diversity methods can be reformulated as Genetic Algorithms where local competition occurs through fitness transformations rather than explicit collection mechanisms. Building on this insight, we introduce Dominated Novelty Search, a Quality-Diversity algorithm that implements local competition through dynamic fitness transformations, eliminating the need for 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 and unsupervised spaces.