# Quality-diversity in dissimilarity spaces

arXiv: https://arxiv.org/abs/2211.12337 · HTML: https://mlsanity.com/paper/2211.12337/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 172 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [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)
- [First return, then explore](https://mlsanity.com/paper/2004.12919/) (2020)
- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space](https://mlsanity.com/paper/1912.02400/) (2019)
- [Using Centroidal Voronoi Tessellations to Scale Up the Multi-dimensional Archive of Phenotypic Elites Algorithm](https://mlsanity.com/paper/1610.05729/) (2016)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [Quality-Diversity Optimization: a novel branch of stochastic optimization](https://mlsanity.com/paper/2012.04322/) (2020)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network](https://mlsanity.com/paper/2007.05674/) (2020)
- [Data-Efficient Design Exploration through Surrogate-Assisted Illumination](https://mlsanity.com/paper/1806.05865/) (2018)
- [Geometric Entropic Exploration](https://mlsanity.com/paper/2101.02055/) (2021)
- [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction](https://mlsanity.com/paper/1908.06760/) (2019)
- [Optimization of the Sherrington-Kirkpatrick Hamiltonian](https://mlsanity.com/paper/1812.10897/) (2018)
- [BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search](https://mlsanity.com/paper/2005.04320/) (2020)
- [Benchmarking Discrete Optimization Heuristics with IOHprofiler](https://mlsanity.com/paper/1912.09237/) (2019)
- [Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters](https://mlsanity.com/paper/2007.05352/) (2020)
- [Low Autocorrelation Binary Sequences](https://mlsanity.com/paper/1512.02475/) (2015)
- [Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection](https://mlsanity.com/paper/2106.00827/) (2021)
- [A Scenario-Based Development Framework for Autonomous Driving](https://mlsanity.com/paper/2011.01439/) (2020)
- [Diversity Enhancement via Magnitude](https://mlsanity.com/paper/2201.10037/) (2022)
- [Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude](https://mlsanity.com/paper/2201.11677/) (2022)
- [An N log N Parallel Fast Direct Solver for Kernel Matrices](https://mlsanity.com/paper/1701.02324/) (2017)
- [Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes](https://mlsanity.com/paper/1908.08020/) (2019)
- [Practical applications of metric space magnitude and weighting vectors](https://mlsanity.com/paper/2006.14063/) (2020)

## Abstract (arXiv)

The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-diversity algorithms in generic dissimilarity spaces. In particular, we instantiate and demonstrate a very general version of Go-Explore with promising performance.