# Dynamic Quality-Diversity Search

arXiv: https://arxiv.org/abs/2404.05769 · HTML: https://mlsanity.com/paper/2404.05769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space](https://mlsanity.com/paper/1912.02400/) (2019)
- [Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network](https://mlsanity.com/paper/2007.05674/) (2020)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries](https://mlsanity.com/paper/1904.10656/) (2019)
- [pyribs: A Bare-Bones Python Library for Quality Diversity Optimization](https://mlsanity.com/paper/2303.00191/) (2023)
- [Multi-Objective Quality Diversity Optimization](https://mlsanity.com/paper/2202.03057/) (2022)
- [Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters](https://mlsanity.com/paper/2007.05352/) (2020)
- [Fast and stable MAP-Elites in noisy domains using deep grids](https://mlsanity.com/paper/2006.14253/) (2020)
- [Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem](https://mlsanity.com/paper/2104.08781/) (2021)
- [Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination](https://mlsanity.com/paper/1702.03713/) (2017)
- [Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms](https://mlsanity.com/paper/2210.13839/) (2022)
- [Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments](https://mlsanity.com/paper/1806.08547/) (2018)

## Abstract (arXiv)

Evolutionary search via the quality-diversity (QD) paradigm can discover highly performing solutions in different behavioural niches, showing considerable potential in complex real-world scenarios such as evolutionary robotics. Yet most QD methods only tackle static tasks that are fixed over time, which is rarely the case in the real world. Unlike noisy environments, where the fitness of an individual changes slightly at every evaluation, dynamic environments simulate tasks where external factors at unknown and irregular intervals alter the performance of the individual with a severity that is unknown a priori. Literature on optimisation in dynamic environments is extensive, yet such environments have not been explored in the context of QD search. This paper introduces a novel and generalisable Dynamic QD methodology that aims to keep the archive of past solutions updated in the case of environment changes. Secondly, we present a novel characterisation of dynamic environments that can be easily applied to well-known benchmarks, with minor interventions to move them from a static task to a dynamic one. Our Dynamic QD intervention is applied on MAP-Elites and CMA-ME, two powerful QD algorithms, and we test the dynamic variants on different dynamic tasks.