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Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and high-performing solutions.
The parallel genetic algorithm as function optimizer
Mühlenbein, H., Schomisch, M., and Born, J. (1991) · 1991
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Multiple hypothesis testing
Shaffer, J. P. (1995) · 1995
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Completely derandomized self-adaptation in evolution strategies
Hansen, N. and Ostermeier, A. (2001) · 2001
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Finite-time analysis of the multiarmed bandit problem
Auer, P., Cesa-Bianchi, N., and Fischer, P. (2002) · 2002
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Gaussian processes in machine learning
Rasmussen, C. E. (2003) · 2003
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A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems
Ali, M. M., Khompatraporn, C., and Zabinsky, Z. B. (2005) · 2005
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Resilient machines through continuous self-modeling
Bongard, J., Zykov, V., and Lipson, H. (2006) · 2006
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Multi-objective test problems, linkages, and evolutionary methodologies
Deb, K., Sinha, A., and Kukkonen, S. (2006) · 2006
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SFERESv2: Evolvin’ in the multi-core world
Mouret, J.-B. and Doncieux, S. (2010) · 2010
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Algorithms for adversarial bandit problems with multiple plays
Uchiya, T., Nakamura, A., and Kudo, M. (2010) · 2010
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On upper-confidence bound policies for switching bandit problems
Garivier, A. and Moulines, E. (2011) · 2011
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Stochastic multi-armed-bandit problem with non-stationary rewards
Besbes, O., Gur, Y., and Zeevi, A. (2014) · 2014
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Disaster robotics
Murphy, R. R. (2014) · 2014
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Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015) · 2015
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Evolving a behavioral repertoire for a walking robot
Cully, A. and Mouret, J.-B. (2015) · 2015
Cited alongside, same era.
Illuminating search spaces by mapping elites
Mouret, J.-B. and Clune, J. (2015) · 2015
Cited alongside, same era.
Confronting the challenge of quality diversity
Pugh, J. K., Soros, L., Szerlip, P. A., and Stanley, K. O. (2015) · 2015
Cited alongside, same era.
Deepmind ai reduces google data centre cooling bill by 40%
Evans, R. and Gao, J. (2016) · 2016
Cited alongside, same era.
Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm
Vassiliades, V., Chatzilygeroudis, K., and Mouret, J.-B. (2018) · 2018
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Discovering the elite hypervolume by leveraging interspecies correlation
Vassiliades, V. and Mouret, J.-B. (2018) · 2018
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Empowering quality diversity in dungeon design with interactive constrained map-elites
Alvarez, A., Dahlskog, S., Font, J., and Togelius, J. (2019) · 2019
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Autonomous skill discovery with quality-diversity and unsupervised descriptors
Cully, A. (2019) · 2019
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Mapping hearthstone deck spaces with map-elites with sliding boundaries
Fontaine, M. C., Lee, S., Soros, L. B., Silva, F. D. M., Togelius, J., and Hoover, A. K. (2019) · 2019
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Hansen, N. (2016) · 2016
Cited alongside, same era.
Springer handbook of robotics (2nd edition)
Siciliano, B. and Khatib, O. (2016) · 2016
Cited alongside, same era.
Aerodynamic design exploration through surrogate-assisted illumination
Gaier, A., Asteroth, A., and Mouret, J.-B. (2017) · 2017
Cited alongside, same era.
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M., Sochat, V., and Bauer, M. W. (2017) · 2017
Cited alongside, same era.
Quality and diversity optimization: A unifying modular framework
Cully, A. and Demiris, Y. (2018) · 2018
Cited alongside, same era.
Data-efficient design exploration through surrogate-assisted illumination
Gaier, A., Asteroth, A., and Mouret, J.-B. (2018) · 2018
Cited alongside, same era.
The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., and Faisal, A. A. (2018) · 2018
Cited alongside, same era.
Are quality diversity algorithms better at generating stepping stones than objective-based search?
Gaier, A., Asteroth, A., and Mouret, J.-B. (2019) · 2019
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Map-elites for noisy domains by adaptive sampling
Justesen, N., Risi, S., and Mouret, J.-B. (2019) · 2019
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Unsupervised learning and exploration of reachable outcome space
Paolo, G., Laflaquiere, A., Coninx, A., and Doncieux, S. (2019) · 2019
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Quantifying the effects of increasing user choice in map-elites applied to a workforce scheduling and routing problem
Urquhart, N., Hart, E., and Hutcheson, W. (2019) · 2019
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Handling bound constraints in cma-es: An experimental study
Biedrzycki, R. (2020) · 2020
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Fast and stable map-elites in noisy domains using deep grids
Flageat, M. and Cully, A. (2020) · 2020
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Covariance matrix adaptation for the rapid illumination of behavior space
Fontaine, M. C., Togelius, J., Nikolaidis, S., and Hoover, A. K. (2020) · 2020
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Discovering representations for black-box optimization
Gaier, A., Asteroth, A., and Mouret, J.-B. (2020) · 2020
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