D. Mark, S. E. Adams, R. A. Celniker, C. A. Holt, J. D. Evans et al., The genome sequence of Drosophila melanogaster, Science, 2000.

L. Altenberg, Evolving better representations through selective genome growth, First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, 1994.

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine learning, 2002.

P. Martin, O. Bendsøe, and . Sigmund, Optimization of structural topology, shape, and material, vol.414, 1995.

C. Josh, R. Bongard, and . Pfeifer, Evolving complete agents using artificial ontogeny, Morpho-functional Machines: The new species, pp.237-258, 2003.

P. Bontrager, W. Lin, J. Togelius, and S. Risi, Deep interactive evolution, International Conference on Computational Intelligence in Music, Sound, Art and Design, 2018.

P. Bontrager, A. Roy, J. Togelius, N. Memon, and A. Ross, Deepmasterprints: Generating masterprints for dictionary attacks via latent variable evolution, IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, pp.1-9, 2018.

P. Christopher, I. Burgess, and . Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner, Understanding disentangling in beta-VAE, 2018.

X. Tian-qi-chen, . Li, B. Roger, D. K. Grosse, and . Duvenaud, Isolating sources of disentanglement in variational autoencoders, Advances in Neural Information Processing Systems, 2018.

D. John, Y. Co-reyes, A. Liu, B. Gupta, P. Eysenbach et al., Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings, Proceedings of the International Conference on Machine Learning (ICML), 2018.

A. Cully, J. Clune, D. Tarapore, and J. Mouret, Robots that can adapt like animals, Nature, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01158243

A. Cully and Y. Demiris, Quality and diversity optimization: A unifying modular framework, IEEE Trans. on Evolutionary Computation, 2018.

L. Dacosta, A. Fialho, M. Schoenauer, and M. Sebag, Adaptive operator selection with dynamic multi-armed bandits, 10th annual conference on Genetic and evolutionary computation, 2008.

J. Kenneth-de, Parameter setting in EAs: a 30 year perspective, Parameter setting in evolutionary algorithms, 2007.

S. Doncieux and J. Meyer, Evolving modular neural networks to solve challenging control problems, Fourth International ICSC Symposium on engineering of intelligent systems, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01501392

P. Durr, D. Floreano, and C. Mattiussi, Genetic representation and evolvability of modular neural controllers, 2010.

T. Elsken, J. H. Metzen, and F. Hutter, Neural Architecture Search: A Survey, Journal of Machine Learning Research, 2019.

C. Matthew, S. Fontaine, . Lee, F. Soros, J. Silva et al., Mapping hearthstone deck spaces through MAP-elites with sliding boundaries, Proceedings of The Genetic and Evolutionary Computation Conference, 2019.

A. Gaier and A. Asteroth, Evolution of optimal control for energy-efficient transport, IEEE Intelligent Vehicles Symposium Proceedings, 2014.

A. Gaier, A. Asteroth, and J. Mouret, Aerodynamic design exploration through surrogate-assisted illumination, 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01518786

A. Gaier, A. Asteroth, and J. Mouret, Data-efficient design exploration through surrogate-assisted illumination, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01817505

A. Gaier and D. Ha, Weight agnostic neural networks, Advances in Neural Information Processing Systems, 2019.

A. Garivier and E. Moulines, On upper-confidence bound policies for switching bandit problems, International Conference on Algorithmic Learning Theory, 2011.

E. David, B. Goldberg, K. Korb, and . Deb, Messy genetic algorithms: Motivation, analysis, and first results, 1989.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Advances in neural information processing systems, 2014.

N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evolutionary computation, 2001.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot et al., Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Machine Learning

E. Geoffrey, . Hinton, R. Ruslan, and . Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, 2006.

S. Hoyer, J. Sohl-dickstein, and S. Greydanus, Neural reparameterization improves structural optimization, 2019.

H. Kim and A. Mnih, Disentangling by Factorising, International Conference on Machine Learning, 2018.

P. Diederik, M. Kingma, and . Welling, Auto-Encoding Variational Bayes, International Conference on Learning Representation (ICLR, 2014.

. John-r-koza, Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems

S. Kullback, A. Richard, and . Leibler, On information and sufficiency. The annals of mathematical statistics, 1951.

Q. David, J. B. Mayne, C. V. Rawlings, P. Rao, and . Scokaert, Constrained model predictive control: Stability and optimality, Automatica, 2000.

J. Risto-miikkulainen, E. Liang, A. Meyerson, D. Rawal, O. Fink et al., Evolving deep neural networks, Artificial Intelligence in the Age of Neural Networks and Brain Computing, 2019.

M. A. Moreno, W. Banzhaf, and C. Ofria, Learning an evolvable genotype-phenotype mapping, Genetic and Evolutionary Computation Conference, 2018.

J. Mouret and J. Clune, Illuminating search spaces by mapping elites, 2015.

J. Mouret and S. Doncieux, MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars, Evolutionary Intelligence, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00687646

M. Olhofer, Y. Jin, and B. Sendhoff, Adaptive encoding for aerodynamic shape optimization using evolution strategies, Congress on Evolutionary Computation, 2001.

K. Justin, L. B. Pugh, K. Soros, and . Stanley, Quality diversity: A new frontier for evolutionary computation, Frontiers in Robotics and AI, 2016.

F. Rothlauf, Representations for genetic and evolutionary algorithms, Representations for Genetic and Evolutionary Algorithms, 2006.

T. Salimans, D. Kingma, and M. Welling, Markov chain monte carlo and variational inference: Bridging the gap, International Conference on Machine Learning, pp.1218-1226, 2015.

O. Eric, J. Scott, and . Bassett, Learning genetic representations for classes of real-valued optimization problems, Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015.

O. Eric, K. Scott, and . Jong, Toward learning neural network encodings for continuous optimization problems, Genetic and Evolutionary Computation Conference Companion, 2018.

B. Shahriari, K. Swersky, Z. Wang, P. Ryan, N. Adams et al., Taking the human out of the loop: A review of Bayesian optimization, Proc. IEEE, 2015.

F. Luís, D. Simões, and . Izzo, Selfadaptive genotype-phenotype maps: neural networks as a meta-representation, International Conference on Parallel Problem Solving from Nature, 2014.

O. Kenneth and . Stanley, Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, 2007.

O. Kenneth, . Stanley, B. David, J. D'ambrosio, and . Gauci, A hypercubebased encoding for evolving large-scale neural networks, Artificial life, 2009.

O. Kenneth, R. Stanley, and . Miikkulainen, Evolving neural networks through augmenting topologies, Evolutionary computation, 2002.

V. Vassiliades, K. Chatzilygeroudis, and J. Mouret, Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm, IEEE Transactions on Evolutionary Computation, vol.22, pp.623-630, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01630627

V. Vassiliades and J. Mouret, Discovering the elite hypervolume by leveraging interspecies correlation, Genetic and Evolutionary Computation Conference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01764739

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is all you need, Advances in neural information processing systems, pp.5998-6008, 2017.

V. Volz, J. Schrum, J. Liu, M. Simon, A. Lucas et al., Evolving mario levels in the latent space of a deep convolutional generative adversarial network, Genetic and Evolutionary Computation Conference, 2018.

S. Wold, K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and intelligent laboratory systems, 1987.

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