- QD: Quality Diversity: A New Frontier for Evolutionary Computation, Justin Pugh et al 2016, Frontiers in Robotics and AI
- QD opt: Quality and diversity optimization: A unifying modular framework , Antoine Cully et al 2018 , TEC
- Policy search in continuous action domains: An overview, Oliver Sigaud et al 2019, Neural Network
- Quality-Diversity Optimization: a novel branch of stochastic optimization, Konstantinos Chatzilygeroudis et al 2021
- An Extended Study of Quality Diversity Algorithms , Justin Pugh et al 2016 , Gecco
- Gaining insight into quality diversity , Joshua Auerbach et al 2016, Gecco
- Searching for quality diversity when diversity is unaligned with quality , Justin Pugh et al 2016 , PPSN
- QD-suite: Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms
- ANALYSIS OF QUALITY DIVERSITY ALGORITHMS FOR THE KNAPSACK PROBLEM, Adel Nikfarjam et al 2022, PPSN
- QD-RL: QD-RL: Efficient Mixing of Quality and Diversity in Reinforcement Learning, Geoffrey Cideron et al 2020
- DQD: Differentiable Quality Diversity, Matthew C. Fontaine et al 2021, NIPS
- GUSS: Guided Safe Shooting: model based reinforcement learning with safety constraints, Giuseppe Paolo et al 2022
- EDO-CS: Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning, Yutong Wang et al 2022, ICLR
- Deep Surrogate Assisted Generation of Environments, Varun Bhatt et al 2022, NIPS
- HTSE: Promoting Quality and Diversity in Population-based Reinforcement Learning via Hierarchical Trajectory Space Exploration, Jiayu Miao et al 2022, ICRA
- DA-QD: Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires, Bryan Lim et al 2022, ICRA
- Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning, Bryon Tjanaka et al 2022, Gecco
- QD-PG: Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization, Thomas Pierrot et al 2022, Gecco
- Discovering evolutionary stepping stones through behavior domination , Elliot Meyerson 2017, Gecco
- The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities , Joel Lehman et al 2018
- Open-ended evolution with multi-containers QD , Stephane Doncieux et al 2018 , Gecco
- Mapping structural diversity in networks sharing a given degree distribution and global clustering: Adaptive resolution grid search evolution with Diophantine equation-based mutations , Peter Overbury et al 2018
- Hierarchical Behavioral Repertoires with Unsupervised Descriptors , Antoine Cully et a; 2018, Gecco
- mEDEA: Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm, Emma Hart 2018, Gecco
- An approach to evolve and exploit repertoires of general robot behaviours,Jorge Gomes et al 2018, Swarm and Evolutionary Computation
- POET:POET: open-ended coevolution of environments and their optimized solutions,Rui Wang et al 2019, Gecco
- Modeling user selection in quality diversity, Alexander Hagg et al 2019, Gecco
- Exploration and Exploitation in Symbolic Regression using Quality-Diversity and Evolutionary Strategies Algorithms, J.-P,Bruneton et al 2019
- Designing neural networks through neuroevolution, Kenneth Stanley et al 2019, Nature Machine Intelligence
- GAPN: Behavioral Repertoire via Generative Adversarial Policy Networks, Marija Jegorova et al 2019
- Autonomous Skill Discovery with Quality-diversity and Unsupervised Descriptors, Antoine Cully 2019, Gecco
- Scaling MAP-Elites to Deep Neuroevolution, Cedric Colas et al 2020
- Quality Diversity for Multi-task Optimization, Jean-Baptiste Mouret et al 2020, Gecco
- Policy Manifold Search for Improving Diversity-based Neuroevolution, Nemanja Rakicevic et al 2020, NIPS Workshop
- Learning behaviour-performance maps with meta-evolution, David Bossens et al 2020, Gecco
- Exploring the Evolution of GANs through Quality Diversity, Victor Costa et al 2020, Gecco
- Enhance POET: Enhanced POET: Open-ended reinforcement learning through unbounded invention of learning challenges and their solutions, Rui Wang et al 2020, ICML
- Effective Diversity in Population Based Reinforcement Learning, Jack Parker-Holder et al 2020, NIPS
- Discovering Representations for Black-box Optimization, Adam Gaier et al 2020, Gecco
- Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations, Szymon Brych et al 2020
- CPPN2GAN: CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation, Jacob Schrum et al 2020, Gecco
- Bop-Elites: Bop-elites, a bayesian optimisation algorithm for quality-diversity search, Paul Kent et al 2020
- Unsupervised Behaviour Discovery with Quality-Diversity Optimisation, Luca Grillotti et al 2021
- Sparse Reward Exploration via Novelty Search and Emitters, Giuseppe Paolo et al 2021, Gecco
- PMS: Policy Manifold Search: Exploring the Manifold Hypothesis for Diversity-based Neuroevolution, Nemanja Rakicevic et al 2021, Gecco
- On the use of feature-maps and parameter control for improved quality-diversity meta-evolution, David M. Bossens et al 2021, Gecco
- Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks, Kirby Steckel et al 2021
- Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search, Alexander Hagg et al 2021, Gecco
- Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms, Leo Cazenille et al 2021, Gecco
- AutoAlpha: AutoAlpha: an Efficient Hierarchical Evolutionary Algorithm for Mining Alpha Factors in Quantitative Investment, Tianping Zhang et al 2021
- Novelty-based multiobjectivization , Jean-Baptiste Mouret 2011
- Evolving a diversity of virtual creatures through novelty search and local competition , Joel Lehman et al 2011, Gecco
- Abandoning objectives: Evolution through the search for novelty alone , Joel Lehman et al 2011, Evolutionary Computation
- Constrained novelty search: A study on game content generation , Antonios Liapis et al 2015, Evolutionary Computation
- Understanding innovation engines: Automated creativity and improved stochastic optimization via deep learning , Anh Nguyen et al 2016, Evolutionary Computation
- Bayesian optimization with automatic prior selection for data-efficient direct policy search, Remi Pautrat et al 2018, ICRA
- Novelty search: a theoretical perspective,Stephane Doncieux et al 2019, Gecco
- BR-NS:BR-NS: an Archive-less Approach to Novelty Search, Achkan Salehi et al 2021, Gecco
- Geodesics, Non-linearities and the Archive of Novelty Search, Achkan Salehi et al 2022, Gecco
- Illuminating search spaces by mapping elites , Jean-Baptiste Mouret et al 2015
- MAP-Elites: Robots that can adapt like animals , Antoine Cully et al 2015 , Nature
- How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm? , Danesh Tarapore et al 2016, Gecco
- SAIL: Feature space modeling through surrogate illumination , Adam Gaier et al 2017, Gecco
- SAIL2: Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination , Adam Gaier et al 2017, Gecco
- Comparing multimodal optimization and illumination , Vassilis Vassiliades et al 2017 , Gecco
- A comparison of illumination algorithms in unbounded spaces , Vassilis Vassiliades et al 2017 , Gecco
- CVT-MAP-Elites: Using Centroidal Voronoi Tessellations to Scale Up the Multidimensional Archive of Phenotypic Elites Algorithm , Vassilis Vassiliades et al 2018 , TEC
- Talakat: Talakat: Bullet Hell Generation through Constrained Map-Elites , Ahmed Khalifa et al 2018 , Gecco
- RTE: Reset-free trial-and-error learning for robot damage recovery , Konstantinos Chatzilygeroudis et al 2018 , RAS
- Optimisation and Illumination of a Real-World Workforce Scheduling and Routing Application (WSRP) via Map-Elites , Neil Urquhart et al 2018, PPSN
- Multi-objective Analysis of MAP-Elites Performance , Eivind Samuelsen et al 2018
- SAIL3: Data-Efficient Design Exploration through Surrogate-Assisted Illumination, Adam Gaier et al 2018, Evolutionary Computation
- MESB: Mapping Hearthstone Deck Spaces with Map-Elites with Sliding Boundaries, Matthew Fontaine et al 2019, Gecco
- MAP-Elites for noisy domains by adaptive sampling, Niels Justesen et al 2019, Gecco
- Evaluating MAP-Elites on Constrained Optimization Problems, Stefano Fioravanzo et al 2019
- Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites, Alberto Alvarez et al 2019
- An illumination algorithm approach to solving the micro-depot routing problem, Neil Urquhart et al 2019, Gecco
- Using MAP-Elites to support policy making around Workforce Scheduling and Routing, Neil Urquhart et al 2020
- Exploring the BipedalWalker benchmark with MAP-Elites and Curiosity-driven A3C, Vikas Gupta et al 2020, Gecco
- Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space, Matthew C. Fontaine et al 2020, Gecco
- PGA-MAP-Elites: Policy Gradient Assisted MAP-Elites, Olle Nilsson et al 2021, Gecco
- Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters, Antoine Cully 2021, Gecco
- Minimize Surprise MAP-Elites: A Task-Independent MAP-Elites Variant for Swarms, Tanja Katharina Kaiser et al 2022, Gecco
- Illuminating Diverse Neural Cellular Automata for Level Generation, Sam Earle et al 2022, Gecco
- Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding, Yulun Zhang et al 2022, Gecco
- Accelerated Quality-Diversity through Massive Parallelism, Bryan Lim et al 2022