E. Alba and M. Tomassini, Parallelism and evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.6, issue.5, pp.443-462, 2002.
DOI : 10.1109/TEVC.2002.800880

URL : http://lslwww.epfl.ch/~marco/ga-ehw-final-SV.ps

E. Alpaydin, Introduction to Machine Learning, 2010.

. Amato, Policy search for multi-robot coordination under uncertainty, The International Journal of Robotics Research, issue.14, pp.351760-1778, 2016.

. Amato, Probabilistic planning for decentralized multi-robot systems, 2015 AAAI Fall Symposium Series, 2015.

. Ammar, Online multitask learning for policy gradient methods, Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp.1206-1214, 2014.

. Ampatzis, Evolution of Signalling in a Group of Robots Controlled by Dynamic Neural Networks, International Workshop on Swarm Robotics, pp.173-188, 2006.
DOI : 10.1007/978-3-540-71541-2_12

. Anderson, Adaptive collective systems: herding black sheep, 2013.

. Argyriou, Multi-task feature learning, Advances in neural information processing systems, pp.41-48, 2007.
DOI : 10.2139/ssrn.1031158

URL : http://www.cs.ucl.ac.uk/staff/m.pontil/reading/mtl_feat.pdf

B. Auerbach, J. Auerbach, and J. C. Bongard, Evolving Monolithic Robot Controllers through Incremental Shaping, New Horizons in Evolutionary Robotics, vol.341, pp.55-65, 2011.
DOI : 10.1007/978-3-642-18272-3_5

URL : http://www.cs.uvm.edu/~jeauerba/publications/auerbach_bongard_iros_2009.pdf

T. Back, Selective pressure in evolutionary algorithms: a characterization of selection mechanisms, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp.57-62, 1994.
DOI : 10.1109/ICEC.1994.350042

H. Balakrishnan, K. Balakrishnan, and V. Honavar, Properties of genetic representations of neural architectures, Proceedings of the World Congress on Neural Networks, p.95, 1995.

. Baldassarre, Evolving Mobile Robots Able to Display Collective Behaviors, Artificial Life, vol.9, issue.3, pp.255-267, 2003.
DOI : 10.1162/106454601753139005

. Baldassarre, Coordination and behaviour integration in cooperating simulated robots, From Animals to Animats V III. Proceedings of the 8 th International Conference on Simulation of Adaptive Behavior. Citeseer, 2004.

S. Bangel and E. Haasdijk, Reweighting rewards in embodied evolution to achieve a balanced distribution of labour, Proceedings of the 14th European Conference on Artificial Life ECAL 2017, 2017.
DOI : 10.7551/ecal_a_012

C. Baray, Evolving cooperation via communication in homogeneous multiagent systems, Proceedings of Intelligent Information Systems, IIS'97, pp.204-208, 1997.

. Bayindir, . ?ahin, L. Bayindir, and E. ?ahin, A review of studies in swarm robotics, Turkish Journal of Electrical Engineering & Computer Sciences, vol.15, issue.2, pp.115-147, 2007.

. Beer, . Gallagher, R. D. Beer, and J. C. Gallagher, Evolving Dynamical Neural Networks for Adaptive Behavior, Adaptive Behavior, vol.1, issue.1, pp.91-122, 1992.
DOI : 10.1007/978-1-4757-1895-9_18

O. Berger-tal and T. Avgar, The Glass is Half-Full: Overestimating the Quality of a Novel Environment is Advantageous, PLoS ONE, vol.105, issue.4, 2012.
DOI : 10.1371/journal.pone.0034578.s003

. Bernard, Evolution of Cooperation in Evolutionary Robotics: the Tradeoff between Evolvability and Efficiency, 07/20/2015-07/24/2015, pp.495-502, 2015.
DOI : 10.7551/978-0-262-33027-5-ch087

URL : https://hal.archives-ouvertes.fr/hal-01314723

. Bernard, To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter, PLOS Computational Biology, vol.365, issue.1553, pp.1-14, 2016.
DOI : 10.1371/journal.pcbi.1004886.s005

URL : https://hal.archives-ouvertes.fr/hal-01317424

. Bonabeau, Swarm Intelligence: From Natural to Artificial Systems, 1999.

. Bonabeau, Quantitative Study of the Fixed Threshold Model for the Regulation of Division of Labour in Insect Societies, Proceedings of the Royal Society B: Biological Sciences, vol.263, issue.1376, pp.2631565-1569, 1376.
DOI : 10.1098/rspb.1996.0229

. Bongard, . Lipson, J. Bongard, and H. Lipson, Once more unto the breach: Co-evolving a robot and its simulator, Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), pp.57-62, 2004.

J. C. Bongard, Reducing collective behavioural complexity through heterogeneity, Artificial Life VII: Proceedings of the Seventh International Conference, pp.327-336, 2000.

. Brambilla, Swarm robotics: a review from the swarm engineering perspective, Swarm Intelligence, vol.2, issue.2???4, pp.1-41, 2013.
DOI : 10.1007/s11721-008-0018-0

URL : https://hal.archives-ouvertes.fr/hal-01405919

J. Branke, Memory enhanced evolutionary algorithms for changing optimization problems, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), pp.1875-1882, 1999.
DOI : 10.1109/CEC.1999.785502

. Branke, A multipopulation approach to dynamic optimization problems, Evolutionary Design and Manufacture, pp.299-307, 2000.

N. Bredèche, Embodied Evolutionary Robotics with Large Number of Robots, Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, pp.1-2, 2014.
DOI : 10.7551/978-0-262-32621-6-ch044

. Bredèche, On-Line, On-Board Evolution of Robot Controllers, Artifical Evolution, pp.110-121, 2010.
DOI : 10.1007/978-3-642-14156-0_10

. Bredèche, Elements of Embodied Evolutionary Robotics, Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, GECCO Companion '15, pp.1247-1247, 2015.
DOI : 10.1016/S0921-8890(02)00170-7

N. Bredèche and J. Montanier, Environment-Driven Embodied Evolution in a Population of Autonomous Agents, Parallel Problem Solving from Nature, pp.290-299, 2010.
DOI : 10.1007/978-3-642-15871-1_30

. Bredèche, Benefits of proportionate selection in embodied evolution, Proceedings of the Genetic and Evolutionary Computation Conference Companion on , GECCO '17, pp.1683-1684, 2017.
DOI : 10.1016/S0921-8890(02)00170-7

. Bredèche, 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

R. Caruana, Multitask Learning: A Knowledge-Based Source of Inductive Bias, Machine Learning, Proceedings of the Tenth International Conference, pp.41-48, 1993.
DOI : 10.1016/B978-1-55860-307-3.50012-5

R. Caruana, Multitask connectionist learning, Proceedings of the Connectionist Models Summer School, p.372, 1994.
DOI : 10.1007/978-1-4615-5529-2_5

R. Caruana, Multitask Learning, Machine Learning, pp.41-75, 1997.
DOI : 10.1007/978-1-4615-5529-2_5

. Chapelle, Semi-Supervised Learning, 2010.
DOI : 10.7551/mitpress/9780262033589.001.0001

. Chatzilygeroudis, Black-box data-efficient policy search for robotics, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
DOI : 10.1109/IROS.2017.8202137

URL : https://hal.archives-ouvertes.fr/hal-01576683

[. Jong and K. A. , An Analysis of the Behavior of a Class of Genetic Adaptive Systems, 1975.

R. Deisenroth, M. Deisenroth, and C. E. Rasmussen, Pilco: A modelbased and data-efficient approach to policy search, Proceedings of the 28th International Conference on machine learning (ICML-11), pp.465-472, 2011.

. Deisenroth, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.2, pp.408-423, 2015.
DOI : 10.1109/TPAMI.2013.218

. Deisenroth, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, vol.2, issue.1-2, pp.1-142, 2013.
DOI : 10.1561/2300000021

G. Deneubourg, J. Deneubourg, and S. Goss, Collective patterns and decision-making, Ethology Ecology & Evolution, vol.3, issue.4, pp.295-311, 1989.
DOI : 10.1111/j.1474-919X.1973.tb01990.x

. Dibangoye, Point-based incremental pruning heuristic for solving finite-horizon Dec-POMDPs, Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems International Foundation for Autonomous Agents and Multiagent Systems, pp.569-576, 2009.

. Dinu, Self-adapting fitness evaluation times for on-line evolution of simulated robots, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, pp.191-198, 2013.
DOI : 10.1145/2463372.2463405

. Doncieux, Evolutionary Robotics: What, Why, and Where to, Frontiers in Robotics and AI, p.4, 2015.
DOI : 10.1177/1059712302010003003

URL : https://hal.archives-ouvertes.fr/hal-01131267

S. Doncieux and J. Mouret, Behavioral diversity measures for Evolutionary Robotics, IEEE Congress on Evolutionary Computation, pp.1303-1310, 2010.
DOI : 10.1109/CEC.2010.5586100

URL : https://hal.archives-ouvertes.fr/hal-00687641

M. Doncieux, 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/s10846-007-9149-6

URL : https://hal.archives-ouvertes.fr/hal-01150254

. Dorigo, Ant colony optimization, IEEE Computational Intelligence Magazine, vol.1, issue.4, pp.28-39, 2006.
DOI : 10.1109/MCI.2006.329691

C. Dorigo, M. Dorigo, and M. Colombetti, Robot shaping: developing autonomous agents through learning, Artificial Intelligence, vol.71, issue.2, pp.321-370, 1994.
DOI : 10.1016/0004-3702(94)90047-7

. Ducatelle, New task allocation methods for robotic swarms, 9th IEEE/RAS Conference on Autonomous Robot Systems and Competitions, 2009.

E. Eberbach, On expressiveness of evolutionary computation: Is EC algorithmic? In Evolutionary Computation, Proceedings of the 2002 Congress on, pp.564-569, 2002.

. Eiben, Embodied, On-line, On-board Evolution for Autonomous Robotics, Symbiotic Multi- Robot Organisms: Reliability, Adaptability, Evolution., volume 7 of Series: Cognitive Systems Monographs, pp.361-382, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00531455

. Eiben, Self-Adaptive Mutation in On-line, On-board Evolutionary Robotics, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop, pp.147-152, 2010.
DOI : 10.1109/SASOW.2010.31

. Eiben, Collective Specialization for Evolutionary Design of a Multi-robot System, International Workshop on Swarm Robotics, pp.189-205, 2006.
DOI : 10.1007/978-3-540-71541-2_13

A. E. Eiben and C. A. Schippers, On evolutionary exploration and exploitation, Fundamenta Informaticae, vol.35, pp.1-435, 1998.

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

. Eker, A finite horizon DEC-POMDP approach to multi-robot task learning, 2011 5th International Conference on Application of Information and Communication Technologies (AICT), pp.1-5, 2011.
DOI : 10.1109/ICAICT.2011.6111001

S. Elfwing, Embodied evolution of learning ability, 2007.

. Elfwing, Darwinian embodied evolution of the learning ability for survival, Adaptive Behavior, vol.19, issue.4, pp.101-120, 2011.
DOI : 10.1016/S0921-8890(02)00170-7

. Ester, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd, pp.226-231, 1996.

P. Evgeniou and M. Pontil, Regularized multi--task learning, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.109-117, 2004.
DOI : 10.1145/1014052.1014067

. Fansi-tchango, Tracking multiple interacting targets using a joint probabilistic data association filter, 17th International Conference on, pp.1-8, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01073429

[. Pérez, Comparison of selection methods in on-line distributed evolutionary robotics, Proceedings of the International Conference on the Synthesis and Simulation of Living Systems (Alife'14), pp.282-289, 2014.

[. Pérez, Decentralized innovation marking for neural controllers in embodied evolution, Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO '15, pp.161-168, 2015.

[. Pérez, Learning collaborative foraging in a swarm of robots using embodied evolution, Proceedings of the 14th European Conference on Artificial Life ECAL 2017, 2017.
DOI : 10.7551/ecal_a_028

. Ferrante, Evolution of Self-Organized Task Specialization in Robot Swarms, PLOS Computational Biology, vol.23, issue.8, p.11, 2015.
DOI : 10.1371/journal.pcbi.1004273.s009

URL : https://hal.archives-ouvertes.fr/hal-01378166

. Ferrauto, Different Genetic Algorithms and the Evolution of Specialization: A Study with Groups of Simulated Neural Robots, Artificial Life, vol.77, issue.2, pp.221-253, 2013.
DOI : 10.1109/37.753932

. Floreano, Neuroevolution: from architectures to learning, Evolutionary Intelligence, vol.87, issue.9, pp.47-62, 2008.
DOI : 10.1093/acprof:oso/9780198524243.003.0001

URL : http://infoscience.epfl.ch/record/112676/files/FloreanoDuerrMattiussi2008.pdf?ln=enversion%3D1

. Floreano, Evolutionary Conditions for the Emergence of Communication in Robots, Current Biology, vol.17, issue.6, pp.17514-519, 2007.
DOI : 10.1016/j.cub.2007.01.058

D. B. Fogel, Phenotypes, genotypes, and operators in evolutionary computation, Proceedings of 1995 IEEE International Conference on Evolutionary Computation, p.193, 1995.
DOI : 10.1109/ICEC.1995.489143

F. , B. Francesca, G. Birattari, and M. , Automatic design of robot swarms: Achievements and challenges, Frontiers in Robotics and AI, vol.3, p.29, 2016.

R. M. French, Semi-distributed Representations and Catastrophic Forgetting in Connectionist Networks, Connection Science, vol.4, issue.3-4, pp.4-7, 1992.
DOI : 10.1037/0033-295X.97.2.285

R. M. French, Catastrophic forgetting in connectionist networks, Trends in Cognitive Sciences, vol.3, issue.4, pp.128-135, 1999.
DOI : 10.1016/S1364-6613(99)01294-2

. From, Vehicle-manipulator systems, 2016.
DOI : 10.1007/978-1-4471-5463-1

M. Gerkey, B. P. Gerkey, and M. J. Matari?, A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems, The International Journal of Robotics Research, vol.23, issue.9, pp.939-954, 2004.
DOI : 10.1016/S0004-3702(99)00025-9

S. Ghosh-dastidar and H. Adeli, SPIKING NEURAL NETWORKS, Spiking neural networks, pp.295-308, 2009.
DOI : 10.1142/S012906570800152X

F. Glover, Tabu Search???Part I, ORSA Journal on Computing, vol.1, issue.3, pp.190-206, 1989.
DOI : 10.1287/ijoc.1.3.190

D. Goldberg, D. E. Goldberg, and K. Deb, A Comparative Analysis of Selection Schemes Used in Genetic Algorithms, Foundations of Genetic Algorithms, pp.69-93, 1991.
DOI : 10.1016/B978-0-08-050684-5.50008-2

R. Goldberg, D. E. Goldberg, and J. Richardson, Genetic algorithms with sharing for multimodal function optimization, Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp.41-49, 1987.

. Gomes, Novelty-Driven Cooperative Coevolution, Evolutionary Computation, vol.25, issue.2, 2015.
DOI : 10.1109/TAMD.2009.2037732

URL : https://repositorio.iscte-iul.pt/bitstream/10071/13859/1/Novelty-Driven%20Cooperative%20Coevolution.pdf

M. Gomez, F. Gomez, and R. Miikkulainen, Incremental Evolution of Complex General Behavior, Adaptive Behavior, vol.5, issue.3-4, pp.317-342, 1997.
DOI : 10.1007/BF00992698

F. Gruau, Genetic synthesis of Boolean neural networks with a cell rewriting developmental process, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks, pp.55-74, 1992.
DOI : 10.1109/COGANN.1992.273948

F. Gruau, Genetic synthesis of modular neural networks, GECCO'93, pp.318-325, 1993.

. Gu, Continuous deep qlearning with model-based acceleration, International Conference on Machine Learning, pp.2829-2838, 2016.

B. Haasdijk, E. Haasdijk, and N. Bredèche, Controlling Task Distribution in MONEE, Advances in Artificial Life, ECAL 2013, pp.671-678, 2013.
DOI : 10.7551/978-0-262-31709-2-ch096

. Haasdijk, Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evolutionary Robotics, PLoS ONE, vol.22, issue.6, pp.1-14, 2014.
DOI : 10.1371/journal.pone.0098466.t001

URL : https://hal.archives-ouvertes.fr/hal-01360915

. Haasdijk, Right on the MONEE, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, pp.207-214, 2013.
DOI : 10.1145/2463372.2463396

. Hamann, Coupled inverted pendulums, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.195-202, 2011.
DOI : 10.1145/2001576.2001604

. Hamann, Artificial hormone reaction networks: Towards higher evolvability in evolutionary multi-modular robotics. arXiv preprint, 2010.

. Hansen, Real-Parameter Black- Box Optimization Benchmarking 2010: Experimental Setup, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00462481

O. Hansen, N. Hansen, and A. Ostermeier, Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation, Proceedings of IEEE International Conference on Evolutionary Computation, pp.312-317, 1996.
DOI : 10.1109/ICEC.1996.542381

G. Hardin, The Tragedy of the Commons???, Journal of Natural Resources Policy Research, vol.10, issue.3, pp.1243-1248, 1968.
DOI : 10.1002/bs.3830010402

. Hastie, Overview of supervised learning, The elements of statistical learning, 2009.

. Hauert, Evolving cooperation: From biology to engineering, The horizons of evolutionary robotics, 2014.

. Hauert, Evolved swarming without positioning information: an??application in aerial communication relay, Autonomous Robots, vol.2, issue.4, pp.21-32, 2009.
DOI : 10.1007/978-3-540-30552-1_11

. Heinerman, Three-fold Adaptivity in Groups of Robots, Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, GECCO '15, pp.177-183, 2015.
DOI : 10.1007/978-3-540-74913-4_29

. Heinerman, Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots, 2015 IEEE Symposium Series on Computational Intelligence, pp.1055-1062, 2015.
DOI : 10.1109/SSCI.2015.152

. Hester, Generalized model learning for Reinforcement Learning on a humanoid robot, 2010 IEEE International Conference on Robotics and Automation, pp.2369-2374, 2010.
DOI : 10.1109/ROBOT.2010.5509181

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

J. H. Holland, Complex adaptive systems, Daedalus, pp.17-30, 1992.

H. Jaeger, A tutorial on training recurrent neural networks covering BPPT, RTRL, EKF, and the Echo-State Network approach, 2008.

. Jakobi, Noise and the reality gap: The use of simulation in evolutionary robotics Advances in artificial life, pp.704-720, 1995.

Z. Jiang, J. Jiang, and C. Zhai, Instance weighting for domain adaptation in nlp, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp.264-271, 2007.

M. Jones, C. Matari?, and M. J. , Adaptive division of labor in large-scale minimalist multi-robot systems, Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1969-1974, 2003.

M. I. Kamien and N. L. Schwartz, Dynamic optimization: the calculus of variations and optimal control in economics and management, 2012.

. Karafotias, An algorithm for distributed on-line, on-board evolutionary robotics, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.171-178, 2011.
DOI : 10.1145/2001576.2001601

J. Kennedy, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.760-766, 2011.
DOI : 10.1109/ICNN.1995.488968

. Kernbach, Specialization and generalization of robot behaviour in swarm energy foraging, Mathematical and Computer Modelling of Dynamical Systems, vol.17, issue.1, pp.131-152, 2012.
DOI : 10.1006/anbe.1996.0467

. Kernbach, Collective adaptive systems: Challenges beyond evolvability, 2011.

. Kim, Autonomous helicopter flight via reinforcement learning, Advances in neural information processing systems, pp.799-806, 2004.

. Kirkpatrick, Optimization by simulated annealing, science, issue.4598, pp.220671-680, 1983.
DOI : 10.1142/9789812799371_0035

URL : http://www.cs.virginia.edu/cs432/documents/sa-1983.pdf

H. Kitano, Designing neural networks using genetic algorithms with graph generation system, Complex systems, vol.4, issue.4, pp.461-476, 1990.

J. Kober and J. R. Peters, Policy search for motor primitives in robotics, Advances in neural information processing systems, pp.849-856, 2009.

T. Kohonen, The self-organizing map, Neurocomputing, vol.21, issue.1-3, pp.1-6, 1998.
DOI : 10.1016/S0925-2312(98)00030-7

V. R. Konda and J. N. Tsitsiklis, Actor-critic algorithms, Advances in neural information processing systems, pp.1008-1014, 2000.

L. Konig and H. Schmeck, A completely evolvable genotypephenotype mapping for evolutionary robotics, Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp.175-185, 2009.

. Koos, The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics, IEEE Transactions on Evolutionary Computation, vol.17, issue.1, pp.1-25, 2012.
DOI : 10.1109/TEVC.2012.2185849

URL : https://hal.archives-ouvertes.fr/hal-00687617

. Kowaliw, Growing Adaptive Machines: Combining Development and Learning in Artificial Neural Networks, 2014.
DOI : 10.1007/978-3-642-55337-0

O. Kramer, Evolutionary self-adaptation: a survey of operators and strategy parameters, Evolutionary Intelligence, vol.11, issue.4, pp.51-65, 2010.
DOI : 10.1287/ijoc.3.4.376

. Labella, Division of labor in a group of robots inspired by ants' foraging behavior, ACM Transactions on Autonomous and Adaptive Systems, vol.1, issue.1, pp.4-25, 2006.
DOI : 10.1145/1152934.1152936

L. Lamport, Time, clocks, and the ordering of events in a distributed system, Communications of the ACM, vol.21, issue.7, pp.558-565, 1978.
DOI : 10.1145/359545.359563

. Lanzi, Learning classifier systems: from foundations to applications, 2003.

. Laureiro-martínez, Understanding the exploration-exploitation dilemma: An fMRI study of attention control and decision-making performance, Strategic Management Journal, vol.13, issue.3, pp.319-338, 2015.
DOI : 10.1287/orsc.13.3.339.2780

P. Lawrence, N. D. Lawrence, and J. C. Platt, Learning to learn with the informative vector machine, Twenty-first international conference on Machine learning , ICML '04, p.65, 2004.
DOI : 10.1145/1015330.1015382

. Li, Learning and Measuring Specialization in Collaborative Swarm Systems, Adaptive Behavior, vol.12, issue.3-4, pp.3-4199, 2004.
DOI : 10.1142/S0219525901000188

W. Liu and A. F. Winfield, Autonomous Morphogenesis in Self-assembling Robots Using IR-Based Sensing and Local Communications, ANTS Conference, pp.107-118, 2010.
DOI : 10.1007/978-3-642-15461-4_10

. Liu, Weighted Task Regularization for Multitask Learning, 2013 IEEE 13th International Conference on Data Mining Workshops, pp.399-406, 2013.
DOI : 10.1109/ICDMW.2013.158

S. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.
DOI : 10.1109/TIT.1982.1056489

. López-ibáñez, The irace package, iterated race for automatic algorithm configuration, 2011.

. Luke, Co-evolving soccer softbot team coordination with genetic programming. RoboCup-97: Robot soccer world cup I, pp.398-411, 1998.
DOI : 10.1007/3-540-64473-3_76

URL : http://www.cs.umd.edu/~seanl/papers/robocupc.ps.gz

W. Maass, Networks of spiking neurons: The third generation of neural network models, Neural Networks, vol.10, issue.9, pp.1659-1671, 1997.
DOI : 10.1016/S0893-6080(97)00011-7

S. W. Mahfoud, Niching Methods for Genetic Algorithms, 1995.

. Manner, Crowding and preselection revisited, Parallel Problem Solving From Nature, pp.27-36, 1992.

. Maron, O. Moore-]-maron, and A. W. Moore, Hoeffding races: Accelerating model selection search for classification and function approximation, Advances in Neural Information Processing Systems 6, pp.59-66, 1994.

. Mattiussi, . Floreano, C. Mattiussi, and D. Floreano, Analog Genetic Encoding for the Evolution of Circuits and Networks, IEEE Transactions on Evolutionary Computation, vol.11, issue.5, pp.596-607, 2007.
DOI : 10.1109/TEVC.2006.886801

M. L. Mauldin, Maintaining diversity in genetic search, AAAI, pp.247-250, 1984.

G. Mc, Maintaining healthy population diversity using adaptive crossover, mutation, and selection, IEEE Transactions on Evolutionary Computation, vol.15, issue.5, pp.692-714, 2011.

P. Mcculloch, W. S. Mcculloch, and W. Pitts, A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, pp.115-133, 1943.

J. Mclurkin and D. Yamins, Dynamic Task Assignment in Robot Swarms, Robotics: Science and Systems I, 2005.
DOI : 10.15607/RSS.2005.I.018

T. M. Mitchell, Machine Learning, 1997.

. Mitchell, Never ending learning, AAAI, pp.2302-2310, 2015.
DOI : 10.1037/e660332010-001

T. Mitchell, T. M. Mitchell, and S. B. Thrun, Explanation-based neural network learning for robot control, Advances in neural information processing systems, pp.287-294, 1993.

B. Montanier, J. Montanier, and N. Bredèche, Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm, New horizons in evolutionary robotics, pp.155-169, 2011.
DOI : 10.1007/978-3-642-18272-3_11

URL : https://hal.archives-ouvertes.fr/inria-00413357

B. Montanier, J. Montanier, and N. Bredèche, Emergence of altruism in open-ended evolution in a population of autonomous agents, Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, GECCO '11, pp.25-26, 2011.
DOI : 10.1145/2001858.2001873

URL : https://hal.archives-ouvertes.fr/inria-00601791

B. Montanier, J. Montanier, and N. Bredèche, Surviving the tragedy of commons: Emergence of altruism in a population of evolving autonomous agents, European Conference on Artificial Life, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00601776

B. Montanier, J. Montanier, and N. Bredèche, Evolution of Altruism and Spatial Dispersion: an Artificial Evolutionary Ecology Approach, Advances in Artificial Life, ECAL 2013, pp.260-267, 2013.
DOI : 10.7551/978-0-262-31709-2-ch040

. Montanier, Behavioral specialization in embodied evolutionary robotics: Why so difficult? Frontiers in Robotics and AI, p.38, 2016.

O. Montes-de, S. Montes-de-oca, M. A. Stützle, and T. , Towards incremental social learning in optimization and multiagent systems, Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation, GECCO '08, pp.1939-1944, 2008.
DOI : 10.1145/1388969.1389004

J. Morimoto and C. G. Atkeson, Minimax differential dynamic programming: application to a biped walking robot, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), pp.1563-1570, 2003.
DOI : 10.1109/IROS.2003.1248926

J. Mouret, Micro-data learning: The other end of the spectrum, ERCIM News, issue.1072, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01374786

D. Mouret, J. Mouret, and S. Doncieux, Evolving modular neuralnetworks through exaptation, Evolutionary Computation CEC'09. IEEE Congress on, pp.1570-1577, 2009.
DOI : 10.1109/cec.2009.4983129

URL : https://hal.archives-ouvertes.fr/hal-00473135

D. Mouret, J. Mouret, and S. Doncieux, Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity, 2009 IEEE Congress on Evolutionary Computation, pp.1161-1168, 2009.
DOI : 10.1109/CEC.2009.4983077

URL : https://hal.archives-ouvertes.fr/hal-00473147

. Murciano, Specialization in multi-agent systems through learning, Biological Cybernetics, vol.76, issue.5, pp.76375-382, 1997.
DOI : 10.1007/s004220050351

. Nelson, Fitness functions in evolutionary robotics: A survey and analysis, Robotics and Autonomous Systems, vol.57, issue.4, pp.345-370, 2009.
DOI : 10.1016/j.robot.2008.09.009

. Nguyen, Evolutionary dynamic optimization: A survey of the state of the art, Swarm and Evolutionary Computation, vol.6, pp.1-24, 2012.
DOI : 10.1016/j.swevo.2012.05.001

G. Nitschke, Emergence of Cooperation: State of the Art, Artificial Life, vol.1, issue.1, pp.367-396, 2005.
DOI : 10.1023/A:1011218919464

. Nitschke, Evolving team behaviors with specialization, Genetic Programming and Evolvable Machines, vol.1, issue.3, pp.493-536, 2012.
DOI : 10.1109/TAMD.2009.2037732

URL : http://www.cs.vu.nl/~gusz/papers/2012-GPaEM-NitschkeEibenSchut.pdf

. Nolfi, . Floreano, S. Nolfi, and D. Floreano, Evolutionary Robotics, 2000.
DOI : 10.1016/S0921-8890(96)00034-6

. Noskov, MONEE: Using Parental Investment to Combine Open-Ended and Task-Driven Evolution, Applications of Evolutionary Computation, 2013.
DOI : 10.1007/978-3-642-37192-9_57

A. Oliehoek, F. A. Oliehoek, and C. Amato, A concise introduction to decentralized POMDPs, 2016.
DOI : 10.1007/978-3-319-28929-8

S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191

. Peters, Relative entropy policy search, AAAI, pp.1607-1612, 2010.

S. Poirier, R. Poirier, and D. L. Silver, Effect of curriculum on the consolidation of neural network task knowledge, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., pp.2123-2128, 2005.
DOI : 10.1109/IJCNN.2005.1556228

. Prieto, Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time, Robotics and Autonomous Systems, vol.58, issue.12, pp.581282-1291, 2010.
DOI : 10.1016/j.robot.2010.08.004

M. Pugh, J. Pugh, and A. Martinoli, Multi-robot learning with particle swarm optimization, Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems , AAMAS '06, pp.441-448, 2006.
DOI : 10.1145/1160633.1160715

W. Rasmussen, C. E. Rasmussen, and C. K. Williams, Gaussian Processes in Machine Learning, 2006.
DOI : 10.1162/089976602317250933

R. Ratcliff, Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions., Psychological Review, vol.97, issue.2, p.285, 1990.
DOI : 10.1037/0033-295X.97.2.285

. Risi, . Togelius, S. Risi, and J. Togelius, Neuroevolution in Games: State of the Art and Open Challenges, IEEE Transactions on Computational Intelligence and AI in Games, vol.9, issue.1, pp.25-41, 2017.
DOI : 10.1109/TCIAIG.2015.2494596

A. Robins, Catastrophic Forgetting;Catastrophic Interference;Stability;Plasticity;Rehearsal., Connection Science, vol.7, issue.2, pp.123-146, 1995.
DOI : 10.1080/09540099550039318

. Rokach, L. Maimon-]-rokach, and O. Maimon, Decision Trees, pp.165-192, 2005.
DOI : 10.1007/0-387-25465-X_9

. Rückstiess, Exploring parameter space in reinforcement learning, Paladyn, vol.1, issue.1, pp.14-24, 2010.

. Rumelhart, Parallel distributed processing: Explorations in the microstructure of cognition, chapter Learning Internal Representations by Error Propagation, pp.318-362, 1986.

N. Rummery, G. A. Rummery, and M. Niranjan, On-line Q-learning using connectionist systems, 1994.

S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2003.

. Ruvolo, . Eaton, P. Ruvolo, and E. Eaton, Active task selection for lifelong machine learning, AAAI, 2013.

. Ruvolo, . Eaton, P. Ruvolo, and E. Eaton, Ella: An efficient lifelong learning algorithm, International Conference on Machine Learning, pp.507-515, 2013.

E. ?ahin and W. M. Spears, International Workshop Swarm Robotics at SAB, 2004.

. Schwarzer, Online evolution in dynamic environments using neural networks in autonomous robots, International Journal On Advances in Intelligent Systems, vol.4, issue.3, pp.288-298, 2012.

. Sehnke, Policy Gradients with Parameter-Based Exploration for Control, Artificial Neural Networks-ICANN 2008, pp.387-396, 2008.
DOI : 10.1007/978-3-540-87536-9_40

. Shah, Gossip Algorithms, Foundations and Trends?? in Networking, vol.3, issue.1, pp.1-125, 2009.
DOI : 10.1561/1300000014

G. I. Sher, Handbook of neuroevolution through Erlang, 2012.
DOI : 10.1007/978-1-4614-4463-3

K. Siciliano, B. Siciliano, and O. Khatib, Springer handbook of robotics, 2016.

. Silva, Speeding Up Online Evolution of Robotic Controllers with Macro-neurons, European Conference on the Applications of Evolutionary Computation, pp.765-776, 2014.
DOI : 10.1007/978-3-662-45523-4_62

. Silva, Online Hyper-evolution of Controllers in Multirobot Systems, 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp.11-20, 2016.
DOI : 10.1109/SASO.2016.7

. Silva, Adaptation of Robot Behaviour through Online Evolution and Neuromodulated Learning, Advances in Artificial Intelligence ? IBERAMIA 2012, pp.300-309, 2012.
DOI : 10.1007/978-3-642-34654-5_31

. Silva, odNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers, Evolutionary Computation, vol.4, issue.3, pp.421-449, 2015.
DOI : 10.1109/5.784219

. Silva, odNEAT: An Algorithm for Distributed Online, Onboard Evolution of Robot Behaviours, Artificial Life 13, pp.251-258, 2012.
DOI : 10.7551/978-0-262-31050-5-ch034

D. L. Silver, The consolidation of task knowledge for lifelong machine learning, AAAI Spring Symposium: Lifelong Machine Learning, 2013.

D. L. Silver and P. Mccracken, The consolidation of neural network task knowledge, Proceedings of the 2003 International Conference on Machine Learning and Applications -ICMLA 2003, pp.185-192, 2003.

P. Silver, D. L. Silver, and R. Poirier, Sequential Consolidation of Learned Task Knowledge, Canadian Conference on AI, pp.217-232, 2004.
DOI : 10.1007/978-3-540-24840-8_16

. Silver, Inductive transfer with context-sensitive neural networks, Machine Learning, p.73313, 2008.
DOI : 10.1007/978-1-4613-2283-2

. Silver, Lifelong machine learning systems: Beyond learning algorithms, AAAI Spring Symposium: Lifelong Machine Learning, pp.49-55, 2013.

. Smith, Quick simulation: a review of importance sampling techniques in communications systems, IEEE Journal on Selected Areas in Communications, vol.15, issue.4, pp.597-613, 1997.
DOI : 10.1109/49.585771

. Sperati, Evolution of Self-organised Path Formation in a Swarm of Robots, ANTS Conference, pp.155-166, 2010.
DOI : 10.1007/978-3-642-15461-4_14

T. Squillero, G. Squillero, and A. Tonda, Divergence of character and premature convergence: A survey of methodologies for promoting diversity in evolutionary optimization, Information Sciences, vol.329, pp.782-799, 2016.
DOI : 10.1016/j.ins.2015.09.056

URL : https://hal.archives-ouvertes.fr/hal-01531616

. Stanley, A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks, Artificial Life, vol.21, issue.2, pp.185-212, 2009.
DOI : 10.1109/5.784219

M. Stanley, K. O. 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://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf

. Steyven, Understanding Environmental Influence in an Open-Ended Evolutionary Algorithm, International Conference on Parallel Problem Solving from Nature, pp.921-931, 2016.
DOI : 10.1145/2739482.2768489

. Steyven, An investigation of environmental influence on the benefits of adaptation mechanisms in evolutionary swarm robotics, Proceedings of the Genetic and Evolutionary Computation Conference on , GECCO '17, 2017.
DOI : 10.1109/TSMCB.2005.859082

P. Stone and M. Veloso, Multiagent systems: A survey from a machine learning perspective, Autonomous Robots, vol.8, issue.3, pp.345-383, 2000.
DOI : 10.21236/ADA333248

. Stradner, Evolving a Novel Bio-inspired Controller in Reconfigurable Robots, European Conference on Artificial Life, pp.132-139, 2009.
DOI : 10.1007/11681120_3

B. Sutton, R. S. Sutton, and A. G. Barto, Time-derivative models of pavlovian reinforcement, pp.497-537, 1990.

B. Sutton, 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

. Sutton, Policy gradient methods for reinforcement learning with function approximation, Advances in neural information processing systems, pp.1057-1063, 2000.

. Tangkaratt, Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation, Neural Networks, vol.57, pp.128-140, 2014.
DOI : 10.1016/j.neunet.2014.06.006

S. Taylor, M. E. Taylor, and P. Stone, Transfer learning for reinforcement learning domains: A survey, Journal of Machine Learning Research, vol.10, pp.1633-1685, 2009.

S. Taylor, M. E. Taylor, and P. Stone, An Introduction to Intertask Transfer for Reinforcement Learning, AI Magazine, vol.32, issue.1, p.15, 2011.
DOI : 10.1609/aimag.v32i1.2329

. Téllez, R. A. Angulo-bahón-téllez, A. Bahón, and C. , Progressive design through staged evolution, 2008.

M. Thrun, S. Thrun, and T. M. Mitchell, Lifelong robot learning, The Biology and Technology of Intelligent Autonomous Agents, pp.25-46, 1995.
DOI : 10.1016/0921-8890(95)00004-Y

P. Thrun, S. Thrun, and L. Pratt, Learning to Learn: Introduction and Overview, Learning to learn, pp.3-17, 1998.
DOI : 10.1007/978-1-4615-5529-2_1

M. Tomassini, Spatially-structured evolutionary algorithms, 2005.

T. , S. Torrey, L. Shavlik, and J. , Transfer learning, Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, p.242, 2009.

C. F. Touzet, Neural reinforcement learning for behaviour synthesis, Robotics and Autonomous Systems, vol.22, issue.3-4, pp.3-4251, 1997.
DOI : 10.1016/S0921-8890(97)00042-0

URL : https://hal.archives-ouvertes.fr/hal-01337989

V. Trianni, Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots (Studies in Computational Intelligence, 2008.
DOI : 10.1007/978-3-540-77612-3

. Trianni, . Nolfi, V. Trianni, and S. Nolfi, Evolving collective control, cooperation and distributed cognition. Handbook of Collective Robotics, pp.127-166, 2009.
DOI : 10.1201/b14908-7

URL : http://laral.istc.cnr.it/nolfi/papers/trianni-nolfi-hbcr-2012.pdf

. Trianni, . Nolfi, V. Trianni, and S. Nolfi, Self-Organizing Sync in a Robotic Swarm: A Dynamical System View, IEEE Transactions on Evolutionary Computation, vol.13, issue.4, pp.722-741, 2009.
DOI : 10.1109/TEVC.2009.2015577

. Trianni, Evolutionary swarm robotics: A theoretical and methodological itinerary from individual neuro-controllers to collective behaviours. The Horizons of Evolutionary Robotics, 2014.
DOI : 10.1007/978-3-540-77612-3

. Trueba, Specialization analysis of embodied evolution for robotic collective tasks, Robotics and Autonomous Systems, vol.61, issue.7, pp.61682-693, 2013.
DOI : 10.1016/j.robot.2012.08.005

E. Tuci, Evolutionary Swarm Robotics: Genetic Diversity, Task-Allocation and Task-Switching, pp.98-109, 2014.
DOI : 10.1007/978-3-319-09952-1_9

. Tuci, Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication, Artificial Life, vol.2, issue.3, pp.157-178, 2008.
DOI : 10.1007/s00422-006-0080-x

. Vassilev, Smoothness, Ruggedness and Neutrality of Fitness Landscapes: from Theory to Application, pp.3-44, 2003.
DOI : 10.1007/978-3-642-18965-4_1

. Waibel, Division of labour and colony efficiency in social insects: effects of interactions between genetic architecture, colony kin structure and rate of perturbations, Proceedings of the Royal Society B: Biological Sciences, vol.17, issue.1393, pp.2731815-1823, 1595.
DOI : 10.1098/rspb.1998.0299

. Waibel, Genetic Team Composition and Level of Selection in the Evolution of Cooperation, IEEE Transactions on Evolutionary Computation, vol.13, issue.3, pp.648-660, 2009.
DOI : 10.1109/TEVC.2008.2011741

. Walker, The balance between initial training and lifelong adaptation in evolving robot controllers, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.2, pp.423-432, 2006.
DOI : 10.1109/TSMCB.2005.859082

. Wang, Unknown environment exploration of multi-robot system with the FORDPSO, Swarm and Evolutionary Computation, vol.26, pp.157-174, 2016.
DOI : 10.1016/j.swevo.2015.09.004

C. J. Watkins, Learning from delayed rewards King's College, 1989.

. Watson, Embodied evolution: Distributing an evolutionary algorithm in a population of robots Robotics and Autonomous Syst, 2002.

G. Weiss, Multiagent systems: a modern approach to distributed artificial intelligence, 1999.

R. J. Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine Learning, pp.3-4229, 1992.

. Wischmann, Embodied evolution and learning: The neglected timing of maturation Advances in Artificial Life, pp.284-293, 2007.

. Yamaguchi, . Atkeson, A. Yamaguchi, and C. G. Atkeson, Neural networks and differential dynamic programming for reinforcement learning problems, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.5434-5441, 2016.
DOI : 10.1109/ICRA.2016.7487755

S. Yang, Memory-based immigrants for genetic algorithms in dynamic environments, Proceedings of the 2005 conference on Genetic and evolutionary computation , GECCO '05, pp.1115-1122, 2005.
DOI : 10.1145/1068009.1068196

X. Yang, Nature-Inspired Metaheuristic Algorithms, 2008.

. Yosinski, Evolving robot gaits in hardware: the hyperneat generative encoding vs. parameter optimization, Proceedings of the European Conference in Artificial Life, 2011.

Y. , S. Yu, E. Suganthan, and P. N. , Evolutionary programming with ensemble of explicit memories for dynamic optimization, IEEE Congress on Evolutionary Computation (CEC'09), pp.431-438, 2009.

R. Zagal, J. C. Zagal, and J. Ruiz-del-solar, Combining Simulation and Reality in Evolutionary Robotics, Journal of Intelligent and Robotic Systems, vol.294, issue.5544, pp.19-39, 2007.
DOI : 10.1177/147470490500300106

D. Zimmer, M. Zimmer, and S. Doncieux, Bootstrapping Q-Learning for Robotics from Neuro-Evolution Results, IEEE Transactions on Cognitive and Developmental Systems, p.99, 2017.
DOI : 10.1109/TCDS.2016.2628817

URL : https://hal.archives-ouvertes.fr/hal-01494744