L. F. Abbott, N. , and S. B. , Synaptic plasticity: taming the beast, Nature Neuroscience, vol.3, issue.Supp, pp.1178-1183, 2000.
DOI : 10.1038/81453

C. Amato, G. Konidaris, G. Cruz, C. A. Maynor, J. P. How et al., Planning for decentralized control of multiple robots under uncertainty, 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015.
DOI : 10.1109/ICRA.2015.7139350

J. Auerbach and J. Bongard, How robot morphology and training order affect the learning of multiple behaviors, 2009 IEEE Congress on Evolutionary Computation, pp.39-46, 2009.
DOI : 10.1109/CEC.2009.4982928

J. E. Auerbach and J. C. Bongard, Evolving complete robots with CPPN-NEAT, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.1475-1482, 2011.
DOI : 10.1145/2001576.2001775

J. E. Auerbach and J. C. Bongard, On the relationship between environmental and morphological complexity in evolved robots, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.521-528, 2012.
DOI : 10.1145/2330163.2330238

J. E. Auerbach and J. C. Bongard, Environmental Influence on the Evolution of Morphological Complexity in Machines, PLoS Computational Biology, vol.43, issue.1, 2014.
DOI : 10.1371/journal.pcbi.1003399.s024

C. H. Bailey, M. Giustetto, Y. Y. Huang, R. D. Hawkins, and E. R. Kandel, Is heterosynaptic modulation essential for stabilizing Hebbian plasticity and memory?, Nature Reviews Neuroscience, vol.1, issue.1, pp.11-201035036191, 1038.
DOI : 10.1038/35036191

T. Bartz-beielstein, Experimental Research in Evolutionary Computation, 2006.

B. Batut, D. P. Parsons, S. Fischer, G. Beslon, C. M. Knibbe et al., In silico experimental evolution: a tool to test evolutionary scenarios Open problems in artificial life, BMC Bioinformatics Artif. Life, vol.14, issue.6, pp.363-37610, 1162.

R. D. Beer, On the Dynamics of Small Continuous-Time Recurrent Neural Networks, Adaptive Behavior, vol.2, issue.3, pp.469-510, 1995.
DOI : 10.1177/105971239500300405

F. Bellas and R. Duro, Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots, IEEE Transactions on Autonomous Mental Development, vol.2, issue.4, pp.340-354, 2010.
DOI : 10.1109/TAMD.2010.2086453

D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein, The Complexity of Decentralized Control of Markov Decision Processes, Mathematics of Operations Research, vol.27, issue.4, 2002.
DOI : 10.1287/moor.27.4.819.297

R. Bianco and S. Nolfi, Toward open-ended evolutionary robotics: evolving elementary robotic units able to self-assemble and self-reproduce, Connection Science, vol.16, issue.4, pp.227-248, 2004.
DOI : 10.1080/09540090412331314759

C. M. Bishop, Neural Networks for Pattern Recognition, 1995.

D. F. Bjorklund, The role of immaturity in human development., Psychological Bulletin, vol.122, issue.2, pp.153-169, 1997.
DOI : 10.1037/0033-2909.122.2.153

J. Bongard, Evolving modular genetic regulatory networks, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), pp.1872-1877, 2002.
DOI : 10.1109/CEC.2002.1004528

J. Bongard, Morphological change in machines accelerates the evolution of robust behavior, Proceedings of the National Academy of Sciences, vol.108, issue.4, pp.1234-1239, 2011.
DOI : 10.1073/pnas.1015390108

J. Bongard, V. Zykov, and H. Lipson, Resilient Machines Through Continuous Self-Modeling, Science, vol.314, issue.5802, pp.1118-1121, 2006.
DOI : 10.1126/science.1133687

J. C. Bongard, The Utility of Evolving Simulated Robot Morphology Increases with Task Complexity for Object Manipulation, Artificial Life, vol.8, issue.3, 2010.
DOI : 10.1080/09540090412331314821

J. C. Bongard, Evolutionary robotics, Communications of the ACM, vol.56, issue.8, 2013.
DOI : 10.1145/2492007.2493883

N. Bredeche, Embodied evolutionary robotics with large number of robots, Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, pp.272-273, 2014.

N. Bredeche, E. Haasdijk, and A. E. Eiben, On-Line, On-Board Evolution of Robot Controllers, Proceedings of the 9th International Conference on Artificial Evolution (Evolution Artificielle ? EA'09), pp.110-121, 2009.
DOI : 10.1007/978-3-642-14156-0_10
URL : https://hal.archives-ouvertes.fr/inria-00413259

N. Bredeche and J. Montanier, Environment-Driven Embodied Evolution in a Population of Autonomous Agents, Parallel Problem Solving from Nature (PPSN), pp.290-299, 2010.
DOI : 10.1007/978-3-642-15871-1_30
URL : https://hal.archives-ouvertes.fr/inria-00506771

N. Bredeche, J. Montanier, W. Liu, and A. F. Winfield, Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents, Mathematical and Computer Modelling of Dynamical Systems, vol.1, issue.3, pp.101-129, 2012.
DOI : 10.1162/EVCO_a_00025
URL : https://hal.archives-ouvertes.fr/inria-00531450

E. Brown, N. Rodenberg, J. Amend, A. Mozeika, E. Steltz et al., Universal robotic gripper based on the jamming of granular material, Proceedings of the National Academy of Sciences, vol.107, issue.44, pp.18809-18814, 2010.
DOI : 10.1073/pnas.1003250107

D. M. Bryson and C. Ofria, Understanding Evolutionary Potential in Virtual CPU Instruction Set Architectures, PLoS ONE, vol.273, issue.12, 2013.
DOI : 10.1371/journal.pone.0083242.t017

E. Bullmore and O. Sporns, Complex brain networks: graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, vol.8, issue.3, pp.186-198, 2009.
DOI : 10.1371/journal.pone.0002051

R. Calandra, J. Peters, and M. P. Deisenroth, An experimental comparison of Bayesian optimization for bipedal locomotion, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp.1-8, 2014.
DOI : 10.1109/ICRA.2014.6907117

J. P. Changeux, P. Courrège, and A. Danchin, A Theory of the Epigenesis of Neuronal Networks by Selective Stabilization of Synapses, Proceedings of the National Academy of Sciences, vol.70, issue.10, pp.2974-2978, 1973.
DOI : 10.1073/pnas.70.10.2974

N. Cheney, J. Clune, and H. Lipson, Evolved electrophysiological soft robots, Proc. of Artificial Life, pp.222-229, 2014.

N. Cheney, R. Maccurdy, J. Clune, and H. Lipson, Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding, Proc. of the International Conference on Genetic and Evolutionary Computation Conference (GECCO'13), (ACM), pp.167-174, 2013.

J. Clune, B. E. Beckmann, P. K. Mckinley, and C. Ofria, Investigating whether hyperNEAT produces modular neural networks, Proceedings of the 12th annual conference on Genetic and evolutionary computation, GECCO '10, pp.635-642, 2010.
DOI : 10.1145/1830483.1830598
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.4870

J. Clune, J. Mouret, and H. Lipson, The evolutionary origins of modularity, Proceedings of the Royal Society B: Biological Sciences, vol.19, issue.2, 2013.
DOI : 10.1162/EVCO_a_00025
URL : https://hal.archives-ouvertes.fr/hal-01300705

O. Coleman, A. Blair, C. , and J. , Automated generation of environments to test the general learning capabilities of AI agents, Proceedings of the 2014 conference on Genetic and evolutionary computation, GECCO '14, pp.161-168, 2014.
DOI : 10.1145/2576768.2598257

S. Collins, A. Ruina, R. Tedrake, and M. Wisse, Efficient Bipedal Robots Based on Passive-Dynamic Walkers, Science, vol.307, issue.5712, pp.1082-1085, 2005.
DOI : 10.1126/science.1107799

G. Cuccu and F. Gomez, When Novelty Is Not Enough, Applications of Evolutionary Computation, pp.234-243, 2011.
DOI : 10.1007/978-3-642-20525-5_24

S. Cussat-blanc and J. Pollack, A cell-based developmental model to generate robot morphologies, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, pp.537-544, 2012.
DOI : 10.1145/2330163.2330240

G. Cybenko and C. Darwin, Approximation by superpositions of a sigmoidal function On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life, Math. Control Signals Syst, pp.303-314, 1859.

E. De-margerie, J. Mouret, S. Doncieux, M. , and J. , Artificial evolution of the morphology and kinematics in a flapping-wing mini-UAV, Bioinspiration & Biomimetics, vol.2, issue.4, pp.65-82, 2007.
DOI : 10.1088/1748-3182/2/4/002

D. C. Dennett, Darwin's Dangerous Idea: Evolution and the Meanings of Life, 1995.

D. C. Dennett, The Baldwin effect: a crane, not a skyhook, Evolution and Learning: The Baldwin Effect Reconsidered, pp.60-79, 2003.

K. P. Dial, Wing-Assisted Incline Running and the Evolution of Flight, Science, vol.299, issue.5605, pp.402-404, 2003.
DOI : 10.1126/science.1078237

S. Doncieux, N. Bredeche, and M. , New Horizons in Evolutionary Robotics, of Studies in Computational Intelligence, 2011.
DOI : 10.1007/978-3-642-18272-3
URL : https://hal.archives-ouvertes.fr/hal-01300712

S. Doncieux, M. , and J. , Evolving modular neural networks to solve challenging control problems, Proceedings of the Fourth International ICSC Symposium on Engineering of Intelligent Systems, pp.1-7, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01501392

S. Doncieux, M. , and J. , Evolving PID-like Neurocontrollers for Nonlinear Control Problems, Control and Intelligent Systems, vol.33, issue.1, pp.55-62, 2005.
DOI : 10.2316/Journal.201.2005.1.201-1555
URL : https://hal.archives-ouvertes.fr/hal-01185692

S. Doncieux and J. Mouret, Beyond black-box optimization: a review of selective pressures for evolutionary robotics, Evolutionary Intelligence, vol.50, issue.1, pp.71-93, 2014.
DOI : 10.1007/s12065-014-0110-x
URL : https://hal.archives-ouvertes.fr/hal-01150254

G. Edelman, Neural Darwinism: The Theory of Neuronal Group Selection, 1987.

A. E. Eiben, E. Haasdijk, and N. Bredeche, Embodied, on-line, on-board evolution for autonomous robotics, Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution, Chap. 5.2, pp.361-382, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00531455

A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, 2003.

C. Espinosa-soto and A. Wagner, Specialization Can Drive the Evolution of Modularity, PLoS Computational Biology, vol.103, issue.3, 2010.
DOI : 10.1371/journal.pcbi.1000719.s008

C. Fernando, E. Szathmary, and P. Husbands, Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal, Frontiers in Computational Neuroscience, vol.6, 2012.
DOI : 10.3389/fncom.2012.00024

S. Ficici, R. Watson, and J. Pollack, Embodied evolution: a response to challenges in evolutionary robotics, Proceedings of the Eighth European Workshop on Learning Robots, pp.14-22, 1999.

D. Floreano, P. Dürr, and C. Mattiussi, Neuroevolution: from architectures to learning, Evolutionary Intelligence, vol.87, issue.9, pp.47-62, 2008.
DOI : 10.1007/s12065-007-0002-4

D. Floreano and L. Keller, Evolution of Adaptive Behaviour in Robots by Means of Darwinian Selection, PLoS Biology, vol.417, issue.1, 2010.
DOI : 10.1371/journal.pbio.1000292.s007

D. Floreano and C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, 2008.

D. Floreano, S. Mitri, S. Magnenat, and L. Keller, Evolutionary Conditions for the Emergence of Communication in Robots, Current Biology, vol.17, issue.6, 2007.
DOI : 10.1016/j.cub.2007.01.058

D. Fogel, Evolutionary Computation, 1998.
DOI : 10.1002/9781119214403.ch10

L. J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, 1966.
DOI : 10.1109/9780470544600.ch7

K. Funahashi, Y. Nakamura, T. Geijtenbeek, M. Van-de-panne, and A. F. Van-der-stappen, Approximation of dynamical systems by continuous time recurrent neural networks, Neural Networks, vol.6, issue.6, pp.801-806, 1145.
DOI : 10.1016/S0893-6080(05)80125-X

J. Gerhart and M. Kirschner, The theory of facilitated variation, Proceedings of the National Academy of Sciences, vol.104, issue.Supplement 1, pp.8582-8589, 2007.
DOI : 10.1073/pnas.0701035104

K. Glette, G. Klaus, J. Zagal, and J. And-torresen, Evolution of locomotion in a simulated quadruped robot and transferral to reality, Proceedings of the Seventeenth International Symposium on Artificial Life and Robotics, pp.1-4, 2012.

C. V. Goldman and S. Zilberstein, Decentralized control of cooperative systems: categorization and complexity analysis, J. Artif. Intell. Res, vol.22, pp.143-174, 2004.

J. Gomes and A. L. Christensen, Generic behaviour similarity measures for evolutionary swarm robotics, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, pp.199-206, 2013.
DOI : 10.1145/2463372.2463398

J. Gomes, P. Urbano, and A. Christensen, Evolution of swarm robotics systems with novelty search. Swarm Intell, pp.115-144, 2013.

J. Gomes, P. Urbano, and A. L. Christensen, Introducing Novelty Search in Evolutionary Swarm Robotics, Proc. of the International Conference on Swarm Intelligence (ANTS'2012), pp.85-96, 2012.
DOI : 10.1007/978-3-642-32650-9_8

S. J. Gould and E. S. Vrba, Exaptation???a Missing Term in the Science of Form, Paleobiology, vol.76, issue.01, pp.4-15, 1982.
DOI : 10.2307/2412538

F. Gruau, Automatic Definition of Modular Neural Networks, Adaptive Behavior, vol.3, issue.2, pp.151-183, 1995.
DOI : 10.1177/105971239400300202

E. Haasdijk, N. Bredeche, and A. E. Eiben, Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evolutionary Robotics, PLoS ONE, vol.22, issue.6, 2014.
DOI : 10.1371/journal.pone.0098466.t001
URL : https://hal.archives-ouvertes.fr/hal-01360915

W. D. Hamilton, The genetical evolution of social behaviour. I, Journal of Theoretical Biology, vol.7, issue.1, pp.1-16, 1964.
DOI : 10.1016/0022-5193(64)90038-4

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, pp.159-195, 2001.
DOI : 10.1016/0004-3702(95)00124-7

F. Hara and R. Pfeifer, Morpho-Functional Machines: The New Species: Designing Embodied Intelligence, 2003.

I. Harvey, P. Husbands, C. , and D. , Seeing the light: artificial evolution; real vision, From Animals to Animats 3: Proceedings of the Third International Conference on Simulation of Adaptive Behavior (SAB), pp.392-401, 1994.

S. Hauert, J. Zufferey, and D. Floreano, Evolved swarming without positioning information: an??application in aerial communication relay, Autonomous Robots, vol.2, issue.4, pp.21-32, 2008.
DOI : 10.1007/s10514-008-9104-9

S. Haykin, Neural Networks: A Comprehensive Foundation, 1998.

V. Heidrich-meisner, Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem, Recent Advances in Reinforcement Learning, pp.136-150, 2008.
DOI : 10.1007/978-3-540-89722-4_11

V. Heidrich-meisner and C. Igel, Neuroevolution strategies for episodic reinforcement learning, Journal of Algorithms, vol.64, issue.4, pp.152-168, 2009.
DOI : 10.1016/j.jalgor.2009.04.002

G. Hinton and S. Nowlan, How learning can guide evolution, Complex Syst, vol.1, pp.495-502, 1987.

J. Holland, Outline for a Logical Theory of Adaptive Systems, Journal of the ACM, vol.9, issue.3, pp.297-314, 1962.
DOI : 10.1145/321127.321128

J. Holland, Outline for a Logical Theory of Adaptive Systems, Journal of the ACM, vol.9, issue.3, pp.297-314, 1962.
DOI : 10.1145/321127.321128

J. Holland and J. And-reitman, Cognitive systems based on adaptive algorithms, ACM SIGART Bulletin, vol.63, issue.63, 1977.
DOI : 10.1145/1045343.1045373

G. S. Hornby and J. B. Pollack, Creating High-Level Components with a Generative Representation for Body-Brain Evolution, Artificial Life, vol.27, issue.3, pp.223-246, 2002.
DOI : 10.1162/106454601300328034

G. S. Hornby, S. Takamura, T. Yamamoto, and M. Fujita, Autonomous evolution of dynamic gaits with two quadruped robots, IEEE Transactions on Robotics, vol.21, issue.3, pp.402-410, 2004.
DOI : 10.1109/TRO.2004.839222

T. Hu and W. Banzhaf, Evolvability and Speed of Evolutionary Algorithms in Light of Recent Developments in Biology, Journal of Artificial Evolution and Applications, vol.128, issue.3, p.568375, 2010.
DOI : 10.1038/nature05874

R. I. Hughes, Models and Representation, Philosophy of scienceSupplement Proceedings of the 1996 Biennial Meetings of the Philosophy of Science Association. Part II: Symposia Papers), pp.325-336, 1997.
DOI : 10.1086/392611

J. Huizinga, J. Mouret, C. , and J. , Evolving neural networks that are both modular and regular, Proceedings of the 2014 conference on Genetic and evolutionary computation, GECCO '14, 2014.
DOI : 10.1145/2576768.2598232
URL : https://hal.archives-ouvertes.fr/hal-01300699

N. Jakobi, Running across the reality gap: Octopod locomotion evolved in a minimal simulation, Evolutionary Robotics, pp.39-58, 1998.
DOI : 10.1007/3-540-64957-3_63

N. Jakobi, P. Husbands, H. , and I. , Noise and the reality gap: The use of simulation in evolutionary robotics, Lect. Notes Comput. Sci, vol.929, pp.704-720, 1995.
DOI : 10.1007/3-540-59496-5_337

Y. Jin, Surrogate-assisted evolutionary computation: Recent advances and future challenges, Swarm and Evolutionary Computation, vol.1, issue.2, pp.61-70, 2011.
DOI : 10.1016/j.swevo.2011.05.001

N. Kashtan and U. And-alon, Spontaneous evolution of modularity and network motifs, Proceedings of the National Academy of Sciences, vol.102, issue.39, 2005.
DOI : 10.1073/pnas.0503610102

S. A. Kauffman, The Origins of Order: Self-Organization and Selection in Evolution, 1993.

M. W. Kirschner and J. C. Gerhart, The Plausibility of Life: Resolving Darwin's Dilemma, 2006.

J. Kober, J. A. Bagnell, and J. Peters, Reinforcement learning in robotics: A survey, The International Journal of Robotics Research, vol.32, issue.11, pp.1238-1274, 2013.
DOI : 10.1177/0278364913495721

J. Kodjabachian, M. , and J. , Evolution and development of neural networks controlling locomotion, gradient-following, and obstacle-avoidance in artificial insects, IEEE Trans. Neural Netw, vol.972, pp.796-812, 1997.

M. Komosinski, The Framsticks system: versatile simulator of 3D agents and their evolution, Kybernetes, vol.32, issue.1/2, pp.156-173, 2003.
DOI : 10.1108/03684920310452382

S. Koos, A. Cully, and J. Mouret, Fast damage recovery in robotics with the T-resilience algorithm, The International Journal of Robotics Research, vol.32, issue.14, pp.1700-1723, 2013.
DOI : 10.1177/0278364913499192
URL : https://hal.archives-ouvertes.fr/hal-00932862

S. Koos, J. Mouret, and S. Doncieux, The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics, IEEE Transactions on Evolutionary Computation, vol.17, issue.1, pp.122-145, 2013.
DOI : 10.1109/TEVC.2012.2185849
URL : https://hal.archives-ouvertes.fr/hal-00687617

T. Kowaliw, N. Bredeche, and R. Doursat, Growing Adaptive Machines, Studies in Computational Intelligence, vol.557, 2014.
DOI : 10.1007/978-3-642-55337-0

P. Krcah, Solving deceptive tasks in robot body-brain co-evolution by searching for behavioral novelty, 2010 10th International Conference on Intelligent Systems Design and Applications, pp.284-289, 2010.
DOI : 10.1109/ISDA.2010.5687250

J. Lehman, S. Risi, D. D. Ambrosio, and K. O. Stanley, Encouraging reactivity to create robust machines, Adaptive Behavior, vol.21, issue.6, pp.484-500, 2013.
DOI : 10.1177/1059712313487390
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.4256

J. Lehman, K. O. Stanley, and R. Miikkulainen, Effective diversity maintenance in deceptive domains, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, pp.215-222, 2013.
DOI : 10.1145/2463372.2463393

J. Lehman and K. O. Stanley, Exploiting open-endedness to solve problems through the search for novelty, Proc. of Artificial Life Conference (ALife XI), pp.329-336, 2008.

J. Lehman and K. O. Stanley, Abandoning Objectives: Evolution Through the Search for Novelty Alone, Evolutionary Computation, vol.7, issue.3, pp.189-223, 2011.
DOI : 10.1016/0165-6074(93)90215-7

J. Lehman and K. O. Stanley, Evolvability Is Inevitable: Increasing Evolvability without the Pressure to Adapt, PLoS ONE, vol.10, issue.4, 2013.
DOI : 10.1371/journal.pone.0062186.g013

R. E. Lenski, C. Ofria, T. C. Collier, and C. Adami, Genome complexity, robustness and genetic interactions in digital organisms, Nature, vol.400, issue.6745, pp.661-6641023245, 1038.
DOI : 10.1038/23245

A. Liapis, G. Yannakakis, and J. Togelius, Enhancements to constrained novelty search, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, pp.343-350, 2013.
DOI : 10.1145/2463372.2463416

H. Lipson, Principles of modularity, regularity, and hierarchy for scalable systems, Journal of Biological Physics and Chemistry, vol.7, issue.4, 2007.
DOI : 10.4024/40701.jbpc.07.04

H. Lipson and J. B. Pollack, Automatic design and manufacture of robotic lifeforms, Nature, vol.406, pp.974-978, 2000.

D. Lizotte, T. Wang, M. Bowling, and D. Schuurmans, Automatic gait optimization with Gaussian process regression, Proc. of International Joint Conference on Artificial Intelligence (IJCAI), pp.944-949, 2007.

J. Long, Darwin's Devices: What Evolving Robots Can Teach us about the History of Life and the Future of Technology, 2012.

P. Macalpine, M. Depinet, and P. Stone, UT Austin Villa 2014: RoboCup 3D simulation league champion via overlapping layered learning, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), pp.1-7, 2015.

A. Maesani, P. R. Fernando, and D. Floreano, Artificial Evolution by Viability Rather than Competition, PLoS ONE, vol.15, issue.1, 2014.
DOI : 10.1371/journal.pone.0086831.s016

M. Mataric and F. Michaud, Behavior-based robotics, Handbook of Robotics, pp.891-909, 2008.

J. Maynard-smith, Byte-sized evolution, Nature, vol.355, issue.6363, pp.772-773, 1992.
DOI : 10.1038/355772a0

E. Mayr, What Evolution Is, Basic Books, 2001.

W. S. Mcculloch, W. Pitts, O. Miglino, H. H. Lund, and S. Nolfi, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-133, 1943.
DOI : 10.1007/BF02478259

W. T. Miller, P. J. Werbos, and R. S. Sutton, Neural Networks for Control, 1995.

S. Mitri, D. Floreano, and L. Keller, The evolution of information suppression in communicating robots with conflicting interests, Proceedings of the National Academy of Sciences, vol.106, issue.37, pp.15786-15790, 2009.
DOI : 10.1073/pnas.0903152106

S. Mitri, S. Wischmann, D. Floreano, and L. Keller, Using robots to understand social behaviour, Biological Reviews, vol.14, issue.1, pp.31-39, 2013.
DOI : 10.1111/j.1469-185X.2012.00236.x

J. Mockus, V. Tiesis, A. Zilinskas, G. Dixon, and . Szego, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, pp.117-129, 1978.

R. Moeckel, Y. N. Perov, A. T. Nguyen, M. Vespignani, S. Bonardi et al., Gait optimization for roombots modular robots — Matching simulation and reality, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.3265-3272, 2013.
DOI : 10.1109/IROS.2013.6696820

J. Montanier and N. Bredeche, Surviving the tragedy of commons: emergence of altruism in a population of evolving autonomous agents, Frontiers in Robotics and AI | Evolutionary Robotics Proc. of the European Conference on Artificial Life, pp.550-557, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00601776

J. Montanier and N. Bredeche, Evolution of altruism and spatial dispersion: an artificial evolutionary ecology approach, Proc. of the European Conference on Artificial Life, pp.260-267, 2013.

J. Mouret, Novelty-Based Multiobjectivization, New Horizons in Evolutionary Robotics: Extended contributions of the 2009 EvoDeRob Workshop, pp.139-154, 2011.
DOI : 10.1007/978-3-642-18272-3_10
URL : https://hal.archives-ouvertes.fr/hal-01300711

J. Mouret and S. Doncieux, MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars, Evolutionary Intelligence, vol.87, issue.9, pp.187-207, 2008.
DOI : 10.1007/s12065-008-0015-7
URL : https://hal.archives-ouvertes.fr/hal-00687646

J. Mouret and S. Doncieux, Evolving modular neural-networks through exaptation, 2009 IEEE Congress on Evolutionary Computation, pp.1570-1577, 2009.
DOI : 10.1109/CEC.2009.4983129
URL : https://hal.archives-ouvertes.fr/hal-00473135

J. Mouret and S. Doncieux, Sferes v2: evolvin-in the multicore world, Proc. of the IEEE World Congress on Computational Intelligence (CEC'10), pp.4079-4086, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00687633

J. Mouret and S. Doncieux, Encouraging Behavioral Diversity in Evolutionary Robotics: An Empirical Study, Evolutionary Computation, vol.341, issue.1, pp.91-133, 2012.
DOI : 10.1016/0020-0190(92)90136-J
URL : https://hal.archives-ouvertes.fr/hal-00687609

J. Mouret, S. Koos, and S. Doncieux, Crossing the reality gap: a short introduction to the transferability approach, ALIFE workshop "Evolution on Physical Systems, pp.1-7, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01300706

J. Mouret and P. Tonelli, Artificial Evolution of Plastic Neural Networks: A Few Key Concepts, Growing Adaptive Machines, pp.251-261, 2014.
DOI : 10.1007/978-3-642-55337-0_9
URL : https://hal.archives-ouvertes.fr/hal-01300702

G. Müller, Evo???devo: extending the evolutionary synthesis, Nature Reviews Genetics, vol.50, issue.12, pp.943-949, 2007.
DOI : 10.1038/nrg2219

S. Nolfi and D. Floreano, Evolutionary Robotics, 2000.
DOI : 10.1007/978-3-319-32552-1_76

M. A. Oliveira, S. Doncieux, J. Mouret, . Peixoto, and C. M. Santos, Optimization of humanoid walking controller: Crossing the reality gap, 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp.1-7, 2013.
DOI : 10.1109/HUMANOIDS.2013.7029963
URL : https://hal.archives-ouvertes.fr/hal-01300704

R. S. Olson, A. Hintze, F. C. Dyer, D. B. Knoester, and C. Adami, Predator confusion is sufficient to evolve swarming behaviour, Journal of The Royal Society Interface, vol.79, issue.2, 2013.
DOI : 10.1017/S1464793103006286

C. H. Papadimitriou, Computational Complexity, 1994.

R. Pfeifer and J. Bongard, How the Body Shapes the Way We Think: A New View of Intelligence, 2007.

M. Pigliucci, Is evolvability evolvable?, Nature Reviews Genetics, vol.49, issue.1, pp.75-82, 2008.
DOI : 10.1038/nrg2278

N. J. Radcliffe, Genetic set recombination and its application to neural network topology optimisation, Artificial Life II, pp.67-90, 1991.
DOI : 10.1007/BF01411376

S. Risi, C. E. Hughes, and K. O. Stanley, Evolving plastic neural networks with novelty search, Adaptive Behavior, vol.18, issue.6, pp.470-491, 2010.
DOI : 10.1177/1059712310379923

S. Risi, S. D. Vanderbleek, C. E. Hughes, and K. O. Stanley, How novelty search escapes the deceptive trap of learning to learn, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO '09, pp.153-160, 2009.
DOI : 10.1145/1569901.1569923

F. Rothlauf, Representations for Genetic and Evolutionary Algorithms, 2006.
DOI : 10.1007/978-3-642-88094-0

R. Y. Rubinstein and D. P. Kroese, The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning, 2004.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2009.

A. Saffiotti, The uses of fuzzy logic in autonomous robot navigation, Soft Computing - A Fusion of Foundations, Methodologies and Applications, vol.1, issue.4, pp.180-197, 1997.
DOI : 10.1007/s005000050020

E. Sahin and W. M. Spears, Swarm Robotics, Lecture Notes in Computer Science, vol.3342, 2005.

A. Saxena, M. Sun, and A. Y. Ng, Make3D: Learning 3D Scene Structure from a Single Still Image, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.5, pp.824-840132, 2008.
DOI : 10.1109/TPAMI.2008.132

. Shadow, Shadow Dexterous Hand Technical Specification, 2013.

N. Shubin, Your Inner Fish: A Journey into the 3.5-Billion-Year History of the Human Body, 2008.

R. Siegwart, I. Nourbakhsh, and D. Scaramuzza, Introduction to Autonomous Mobile Robots, 2011.

K. Sims, Evolving virtual creatures, Proceedings of the 21st annual conference on Computer graphics and interactive techniques , SIGGRAPH '94, pp.15-22, 1994.
DOI : 10.1145/192161.192167
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.2734

B. Skyrms, The Stag Hunt and the Evolution of Social Structure, 2003.
DOI : 10.1017/CBO9781139165228

M. Solomon, T. Soule, and R. B. Heckendorn, A comparison of a communication strategies in cooperative learning, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, p.153, 2012.
DOI : 10.1145/2330163.2330185

A. Soltoggio, J. A. Bullinaria, C. Mattiussi, P. Dürr, and D. Floreano, Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios, Proc. of Artificial Life Conference (ALife XI), p.11, 2008.

A. Soltoggio, P. Durr, C. Mattiussi, and D. Floreano, Evolving neuromodulatory topologies for reinforcement learning-like problems, 2007 IEEE Congress on Evolutionary Computation, pp.2471-2478, 2007.
DOI : 10.1109/CEC.2007.4424781

L. Soros and K. Stanley, Identifying necessary conditions for open-ended evolution through the artificial life world of chromaria, Proc. of Artificial Life Conference (ALife 14), pp.793-800, 2014.

K. Stanley and R. Miikkulainen, Evolving Neural Networks through Augmenting Topologies, Evolutionary Computation, vol.7, issue.2, pp.99-127, 2002.
DOI : 10.1016/S0096-3003(97)10005-4
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.4591

K. O. Stanley, D. B. D-'ambrosio, and J. Gauci, A hypercube-based indirect encoding for evolving large-scale neural networks, Artif. Life, vol.15, 2009.

K. O. Stanley and R. Miikkulainen, A Taxonomy for Artificial Embryogeny, Artificial Life, vol.13, issue.1, pp.93-130, 2003.
DOI : 10.1002/cne.902460104

F. Stulp and O. Sigaud, Path integral policy improvement with covariance matrix adaptation, Proc. of the 29th International Conference on Machine Learning (ICML-12), pp.281-288, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00789391

F. Stulp and O. Sigaud, Abstract, Paladyn, Journal of Behavioral Robotics, vol.4, issue.1, pp.49-61, 2013.
DOI : 10.2478/pjbr-2013-0003

N. P. Suh, The Principles of Design, 1990.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

D. Tarapore and J. Mouret, Comparing the evolvability of generative encoding schemes, Proc. of Artificial Life Conference (ALife 14), pp.55-62, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01300700

D. Tarapore and J. Mouret, Evolvability signatures of generative encodings: Beyond standard performance benchmarks, Information Sciences, vol.313, pp.1-20, 2014.
DOI : 10.1016/j.ins.2015.03.046
URL : https://hal.archives-ouvertes.fr/hal-01136711

M. Taylor, S. Whiteson, and P. Stone, Temporal difference and policy search methods for reinforcement learning: an empirical comparison, Proc. of AAAI Conference, pp.1675-1678, 2007.

M. E. Taylor, S. Whiteson, and P. Stone, Comparing evolutionary and temporal difference methods in a reinforcement learning domain, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, p.1321, 2006.
DOI : 10.1145/1143997.1144202

S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics, Communications of the ACM, vol.45, issue.3, 2005.
DOI : 10.1145/504729.504754

J. Togelius, T. Schaul, D. Wierstra, C. Igel, and J. Schmidhuber, Ontogenetic and phylogenetic reinforcement learning, Kuenstliche Intelligenz, vol.9, pp.30-33, 2009.

M. Tomassini, Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series), 2005.

P. Tonelli and J. Mouret, On the relationships between synaptic plasticity and generative systems, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.1531-1538, 2011.
DOI : 10.1145/2001576.2001782
URL : https://hal.archives-ouvertes.fr/hal-00687621

P. Tonelli and J. Mouret, On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks, PLoS ONE, vol.315, issue.11, 2013.
DOI : 10.1371/journal.pone.0079138.s001
URL : https://hal.archives-ouvertes.fr/hal-01264752

V. Trianni, Evolutionary robotics: model or design? Front. Robot, 2014.

V. Trianni, S. Nolfi, and M. Dorigo, Evolution, self-organisation and swarm robotics, Swarm Intelligence, pp.163-191, 2008.
DOI : 10.1007/978-3-540-74089-6_5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.7399

D. Trivedi, C. D. Rahn, W. M. Kier, and I. D. Walker, Soft Robotics: Biological Inspiration, State of the Art, and Future Research, Applied Bionics and Biomechanics, vol.5, issue.3, pp.99-11710, 1080.
DOI : 10.1155/2008/520417

G. Turkewitz, K. , and P. A. , The Role of Developmental Limitations of Sensory Input on Sensory/Perceptual Organization, Journal of Developmental & Behavioral Pediatrics, vol.6, issue.5, pp.302-306, 1985.
DOI : 10.1097/00004703-198510000-00014

J. Urzelai and D. Floreano, Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments, Evolutionary Computation, vol.4, issue.4, pp.495-524, 2001.
DOI : 10.1162/neco.1990.2.1.85

P. Verbancsics and K. O. Stanley, Constraining connectivity to encourage modularity in HyperNEAT, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.1483-1490, 2011.
DOI : 10.1145/2001576.2001776

G. Wagner, A. , and L. , Perspective: Complex Adaptations and the Evolution of Evolvability, Evolution, vol.50, issue.3, pp.967-976, 1996.
DOI : 10.2307/2410639

G. P. Wagner, M. Pavlicev, C. , and J. M. , The road to modularity, Nature Reviews Genetics, vol.24, issue.12, pp.921-93110, 1038.
DOI : 10.1038/nrg2267

M. Waibel, D. Floreano, and L. Keller, A Quantitative Test of Hamilton's Rule for the Evolution of Altruism, PLoS Biology, vol.164, issue.5, 2011.
DOI : 10.1371/journal.pbio.1000615.t001

M. Waibel, L. Keller, and D. Floreano, Genetic Team Composition and Level of Selection in the Evolution of Cooperation, IEEE Transactions on Evolutionary Computation, vol.13, issue.3, pp.648-660, 2008.
DOI : 10.1109/TEVC.2008.2011741

R. A. Watson, S. G. Ficici, and J. B. Pollack, Embodied Evolution: Distributing an evolutionary algorithm in a population of robots, Robotics and Autonomous Systems, vol.39, issue.1, pp.1-18, 2002.
DOI : 10.1016/S0921-8890(02)00170-7

S. Whiteson, Evolutionary Computation for Reinforcement Learning, Reinforcement Learning: State of the Art, pp.1-30, 2012.
DOI : 10.1007/978-3-642-27645-3_10

S. Wischmann, D. Floreano, and L. Keller, Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots, Proceedings of the National Academy of Sciences, vol.109, issue.3, pp.864-868, 2012.
DOI : 10.1073/pnas.1104267109

M. J. Wiser, N. Ribeck, and R. E. Lenski, Long-Term Dynamics of Adaptation in Asexual Populations, Science, vol.342, issue.6164, pp.1364-1367, 2013.
DOI : 10.1126/science.1243357

R. J. Woods, J. E. Barrick, T. F. Cooper, U. Shrestha, M. R. Kauth et al., Second-Order Selection for Evolvability in a Large Escherichia coli Population, Science, vol.331, issue.6023, pp.1433-1436, 2011.
DOI : 10.1126/science.1198914

X. Yao, Evolving artificial neural networks, Proc. IEEE, pp.1423-1447, 1999.

J. Yosinski, J. Clune, D. Hidalgo, S. Nguyen, J. Zagal et al., Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization, Proc. of the European Conference on Artificial Life (ECAL), pp.1-8, 2011.

J. C. Zagal, R. Solar, and J. , Combining Simulation and Reality in Evolutionary Robotics, Journal of Intelligent and Robotic Systems, vol.294, issue.5544, pp.19-39, 2007.
DOI : 10.1007/s10846-007-9149-6

T. Ziemke and M. Thieme, Neuromodulation of Reactive Sensorimotor Mappings as a Short-Term Memory Mechanism in Delayed Response Tasks, Adaptive Behavior, vol.10, issue.3-4, pp.185-199, 2002.
DOI : 10.1177/1059712302919993003

V. Zykov, A. Chan, and H. Lipson, Molecubes: an open-source modular robotics kit, IROS-2007 Self-Reconfigurable Robotics Workshop, pp.3-6, 2007.