Evolutionary Robotics

  • Stefano Nolfi
  • Josh Bongard
  • Phil Husbands
  • Dario Floreano
Part of the Springer Handbooks book series (SHB)


Evolutionary Robotics is a method for automatically generating artificial brains and morphologies of autonomous robots. This approach is useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes. In this chapter we provide an overview of methods and results of Evolutionary Robotics with robots of different shapes, dimensions, and operation features. We consider both simulated and physical robots with special consideration to the transfer between the two worlds.


Control Policy Body Plan Infrared Sensor Real Robot Neural Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





artificial neural network


charge-coupled device


direct current


dynamic state machine


Ecole Polytechnique Fédérale de Lausanne


evolutionary robotics


framework for autonomous robotics simulation and analysis


field-programmable gate array


neural network


programmable intelligent computer


programmable logic device


read-only memory


  1. 76.1
    S. Nolfi, D. Floreano: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (MIT/Bradford, Cambridge 2000)Google Scholar
  2. 76.2
    J.H. Holland: Adaptation in Natural and Artificial Systems (Univ. of Michigan Press, Ann Arbor 1975)Google Scholar
  3. 76.3
    A.M. Turing: Computing machinery and intelligence, Mind LIX 236, 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  4. 76.4
    V. Braitenberg: Vehicles. Experiments in Synthetic Psychology (MIT, Cambridge 1984)Google Scholar
  5. 76.5
    R.D. Beer: Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology (Academic, Boston 1990)zbMATHGoogle Scholar
  6. 76.6
    D. Parisi, F. Cecconi, S. Nolfi: Econets: Neural networks that learn in an environment, Network 1, 149–168 (1990)CrossRefGoogle Scholar
  7. 76.7
    P. Husbands, I. Harvey: Evolution versus design: Controlling autonomous robots, Integrating Percept. Plan. Action, Proc. 3rd IEEE Annu. Conf. Artif. Intell. Simul. Plan. (1992) pp. 139–146Google Scholar
  8. 76.8
    D. Floreano, O. Miglino, D. Parisi: Emergent complex behaviors in ecosystems of neural networks. In: Parallel Architectures and Neural Networks, ed. by E. Caianiello (World Scientific, Singapore 1991)Google Scholar
  9. 76.9
    R.A. Brooks: Intelligence without representation, Artif. Intell. 47, 139–159 (1991)CrossRefGoogle Scholar
  10. 76.10
    F. Mondada, E. Franzi, P. Ienne: Mobile robot miniaturization: A tool for investigation in control algorithms, Proc. 3rd Int. Symp. Exp. Robotics, Tokyo, ed. by T. Yoshikawa, F. Miyazaki (1993) pp. 501–513Google Scholar
  11. 76.11
    L. Steels (Ed.): The Biology and Technology of Intelligent Autonomous Agents, NATO ASI (Springer, Berlin, Heidelberg 1995)Google Scholar
  12. 76.12
    D. Floreano, F. Mondada: Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot, Proc. 3rd Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 3, ed. by D. Cliff, P. Husbands, J.A. Meyer, S.W. Wilsonpages (MIT, Cambridge 1994) pp. 402–410Google Scholar
  13. 76.13
    I. Harvey, P. Husbands, D.T. Cliff: Seeing the light: Artificial evolution, real vision, Proc. 3rd Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 3, ed. by D.T. Cliff, P. Husbands, J.-A. Meyer, S. Wilson (MIT, Cambridge 1994) pp. 392–401Google Scholar
  14. 76.14
    M.A. Lewis, A.H. Fagg, A. Solidum: Genetic programming approach to the construction of a neural network for a walking robot, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (1992) pp. 2618–2623Google Scholar
  15. 76.15
    D. Cliff, I. Harvey, P. Husbands: Explorations in evolutionary robotics, Adapt. Behav. 2, 73–110 (1993)CrossRefGoogle Scholar
  16. 76.16
    D.E. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading City 1989)zbMATHGoogle Scholar
  17. 76.17
    H. de Garis: Genetic programming: Evolution of time dependent neural network modules which teach a pair of stick legs to walk, Proc. 9th Eur. Conf. Artif. Intell. (ECAI), Stock. (1990) pp. 204–206Google Scholar
  18. 76.18
    R.D. Beer, J.C. Gallagher: Evolving dynamical neural networks for adaptive behavior, Adapt. Behav. 1, 94–110 (1992)CrossRefGoogle Scholar
  19. 76.19
    R.D. Beer, H.J. Chiel, L.S. Sterling: Heterogeneous neural networks for adaptive behavior in dynamic environments. In: Neural Information Processing Systems, Vol. 1, ed. by D. Touretzky (Morgan Kauffman, San Mateo 1989) pp. 577–585Google Scholar
  20. 76.20
    M.A. Lewis, A.H. Fagg, G. Bekey: Genetic algorithms for gait synthesis in a hexapod robot. In: Recent Trends in Mobile Robots, ed. by Y. Zheng (World Scientific, Singapore 1994) pp. 317–331CrossRefGoogle Scholar
  21. 76.21
    J. Gallagher, R. Beer, M. Espenschiel, R. Quinn: Application of evolved locomotion controllers to a hexapod robot, Robotics Auton. Syst. 19(1), 95–103 (1996)CrossRefGoogle Scholar
  22. 76.22
    R.D. Beer, R.D. Quinn, H.J. Chiel, R.E. Ritzmann: Biologically inspired approaches to robotics, Commun. ACM 40, 31–38 (1997)CrossRefGoogle Scholar
  23. 76.23
    S. Galt, B.L. Luk, A.A. Collie: Evolution of smooth and efficient walking motions for an 8-legged robot, Proc. 6th Eur. Workshop Learn. Robots, Brighton (1997)Google Scholar
  24. 76.24
    T. Gomi, K. Ide: Emergence of gaits of a legged robot by collaboration through evolution, IEEE World Congr. Comput. Intell. (IEEE Press, New York 1998)Google Scholar
  25. 76.25
    F. Gruau: Automatic definition of modular neural networks, Adapt. Behav. 3(2), 151–183 (1995)CrossRefGoogle Scholar
  26. 76.26
    F. Gruau, K. Quatramaran: Cellular encoding for interactive evolutionary robotics, Proc. 4th Eur. Conf. Artif. Life, ed. by P. Husbands, I. Harvey (MIT, Cambridge 1997) pp. 368–377Google Scholar
  27. 76.27
    J. Kodjabachian, J.A. Meyer: Evolution and development of neural networks controlling locomotion, gradient following and obstacle avoidance in artificial insects, IEEE Trans. Neural Netw. 9, 796–812 (1998)CrossRefGoogle Scholar
  28. 76.28
    N. Jakobi: Running across the reality gap: Octopod locomotion evolved in a minimal simulation, Lect. Notes Comput. Sci. 1468, 39–58 (1998)CrossRefGoogle Scholar
  29. 76.29
    R. Téllez, C. Angulo, D. Pardo: Evolving the walking behavior of a 12 DOF quadruped using a distributed neural architecture, Lect. Notes Comput. Sci. 3853, 5–19 (2006)CrossRefGoogle Scholar
  30. 76.30
    T. Reil, P. Husbands: Evolution of central pattern generators for bipedal walking in real-time physics environments, IEEE Trans. Evol. Comput. 6(2), 10–21 (2002)CrossRefGoogle Scholar
  31. 76.31
  32. 76.32
    B. von Haller, A.J. Ijspeert, D. Floreano: Co-evolution of structures and controllers for Neubot underwater modular robots, Lect. Notes Comput. Sci. 3630, 189–199 (2005)CrossRefGoogle Scholar
  33. 76.33
    E. Vaughan, E.A. Di Paolo, I. Harvey: The evolution of control and adaptation in a 3D powered passive dynamic walker, Proc. 9th Int. Conf. Simul. Synth. Living Syst. Artif. Life IX, ed. by J. Pollack, M. Bedau, P. Husbands, T. Ikegami, R. Watson (MIT, Cambridge 2004) pp. 139–145Google Scholar
  34. 76.34
    T. McGeer: Passive walking with knees, Proc. IEEE Conf. Robotics Autom. (ICRA) (1990) pp. 1640–1645CrossRefGoogle Scholar
  35. 76.35
    S. Wischmann, F. Passeman: From passive to active dynamic 3D bipedal walking – An evolutionary approach, Proc. 7th Int. Conf. Climbing Walk. Robots (CLAWAR 2004), ed. by M. Armada, P. González de Santos (Springer, Berlin, Heidelberg 2005) pp. 737–744CrossRefGoogle Scholar
  36. 76.36
    E. Vaughan, E.A. Di Paolo, I. Harvey: The tango of a load balancing biped, Proc. 7th Int. Conf. Climbing Walk. Robots (CLAWAR), ed. by M. Armada, P. González de Santos (2005)Google Scholar
  37. 76.37
    K. Endo, F. Yamasaki, T. Maeno, H. Kitano: A method for co-evolving morphology and walking pattern of biped humanoid robot, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (2002) pp. 2775–2780Google Scholar
  38. 76.38
    G. McHale, P. Husbands: Quadrupedal locomotion: Gasnets, CTRNNs and hybrid CTRNN/PNNs compared, Proc. 9th Int. Conf. Simul. Synth. Living Syst. (Artif. Life IX), ed. by J. Pollack, M. Bedau, P. Husbands, T. Ikegami, R. Watson (MIT, Cambridge 2004) pp. 106–112Google Scholar
  39. 76.39
    G. McHale, P. Husbands: GasNets and other evolvable neural networks applied to bipedal locomotion, Proc. 8th Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 8, ed. by S. Schaal (MIT, Cambridge 2004) pp. 163–172Google Scholar
  40. 76.40
    J.F. Laszlo, M. van de Panne, E. Fiume: Limit cycle control and its application to the animation of balancing and walking, Proc. 23rd Annu. Conf. Comp. Graph. Interact. Tech., ACM (1996) pp. 155–162Google Scholar
  41. 76.41
    R.A. Brooks: Artificial life and real robots, Proc. 1st Eur. Conf. Artif. Life., Toward a Pract. Auton. Syst., ed. by F.J. Varela, P. Bourgine (MIT, Cambridge 1992) pp. 3–10Google Scholar
  42. 76.42
    R. Featherstone, D. Orin: Robot dynamics: Equations and algorithms, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (2000) pp. 826–834Google Scholar
  43. 76.43
    N. Jakobi, P. Husbands, I. Harvey: Noise and the reality gap: The use of simulation in evolutionary robotics, Lect. Notes Comput. Sci. 929, 704–720 (1995)CrossRefGoogle Scholar
  44. 76.44
    O. Miglino, H.H. Lund, S. Nolfi: Evolving mobile robots in simulated and real environments, Artif. Life 2, 417–434 (1996)CrossRefGoogle Scholar
  45. 76.45
    N. Jakobi: Half-baked, ad-hoc and noisy: Minimal simulations for evolutionary robotics, Proc. 4th Eur. Conf. Art. Life, ed. by P. Husbands, I. Harvey (MIT, Cambridge 1997) pp. 348–357Google Scholar
  46. 76.46
    J.C. Bongard, H. Lipson: Nonlinear system identification using coevolution of models and tests, IEEE Trans. Evol. Comput. 9(4), 361–384 (2005)zbMATHCrossRefGoogle Scholar
  47. 76.47
    S. Koos, J. Mouret, S. Doncieux: Crossing the reality gap in evolutionary robotics by promoting transferable controllers, Proc. 12th Annu. Conf. Genetic Evol. Comput. ACM (2010) pp. 119–126Google Scholar
  48. 76.48
    J. Urzelai, D. Floreano: Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments, Evol. Comput. 9, 495–524 (2001)zbMATHCrossRefGoogle Scholar
  49. 76.49
    H.R. Maturana, F.J. Varela: Autopoiesis and Cognition: The Realization of the Living (Reidel, Dordrecht 1980)CrossRefGoogle Scholar
  50. 76.50
    R.D. Beer: A dynamical systems perspective on agent-environment interaction, Artif. Intell. 72, 173–215 (1995)CrossRefGoogle Scholar
  51. 76.51
    P. Funes, B. Orme, E. Bonabeau: Evolving emergent group behaviors for simple humans agents, Proc. 7th Eur. Conf. Artif. Life, ed. by J. Dittrich, T. Kim (Springer, Berlin, Heidelberg 2003) pp. 76–89Google Scholar
  52. 76.52
    S. Nolfi: Behavior and cognition as a complex adaptive system: Insights from robotic experiments. In: Philosophy of Complex Systems, ed. by C. Hooker (Elsevier, Amsterdam 2009) pp. 443–466Google Scholar
  53. 76.53
    S. Nolfi: Power and limits of reactive agents, Neurocomputing 42, 119–145 (2002)zbMATHCrossRefGoogle Scholar
  54. 76.54
    E. Tuci, T. Ferrauto, A. Zeschel, G. Massera, S. Nolfi: An Experiment on behaviour generalisation and the emergence of linguistic compositionality in evolving robots, IEEE Trans. Auton. Mental Dev. 3, 176–189 (2011)CrossRefGoogle Scholar
  55. 76.55
    C. Scheier, R. Pfeifer, Y. Kunyioshi: Embedded neural networks: Exploiting constraints, Neural Netw. 11, 1551–1596 (1998)CrossRefGoogle Scholar
  56. 76.56
    S. Nolfi, D. Marocco: Active perception: A sensorimotor account of object categorization, Proc. 7th Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 7, ed. by B. Hallam, D. Floreano, J. Hallam, G. Hayes, J.-A. Meyer (MIT, Cambridge, MA 2002) pp. 266–271Google Scholar
  57. 76.57
    E. Tuci, G. Massera, S. Nolfi: Active categorical perception of object shapes in a simulated anthropomorphic robotic arm, IEEE Trans. Evol. Comput. 14, 885–899 (2010)CrossRefGoogle Scholar
  58. 76.58
    S. Collins, A. Ruina, R. Tedrake, M. Wisse: Efficient bipedal robots based on passive-dynamic walkers, Science 307(5712), 1082–1085 (2005)CrossRefGoogle Scholar
  59. 76.59
    J.C. Bongard: Innocent until proven guilty: Reducing robot shaping from polynomial to linear time, IEEE Trans. Evol. Comput. 15(4), 571–585 (2011)CrossRefGoogle Scholar
  60. 76.60
    H. Lipson, J.B. Pollack: Automatic design and manufacture of artificial lifeforms, Nature 406, 974–978 (2000)CrossRefGoogle Scholar
  61. 76.61
    K. Sims: Evolving 3D morphology and behaviour by competition, Artif. Life 1(4), 28–39 (1994)CrossRefGoogle Scholar
  62. 76.62
    Karl Sims: Evolved virtual creatures, evolution simulation, (1994)
  63. 76.63
    P. Funes, J. Pollack: Evolutionary body building: Adaptive physical designs for robots, Artif. Life 4(4), 337–357 (1998)CrossRefGoogle Scholar
  64. 76.64
  65. 76.65
    J. Long: Darwin's devices: What evolving robots can teach us about the history of life and the future of technology (Basic Books, New York 2012)Google Scholar
  66. 76.66
    A.J. Clark, J.M. Moore, J. Wang, X. Tan, P.K. McKinley: Evolutionary design and experimental validation of a flexible caudal fin for robotic fish, Artif. Life 13, 325–332 (2012)Google Scholar
  67. 76.67
    J. Bongard: Morphological change in machines accelerates the evolution of robust behavior, Proc. Natl. Acad. Sci. 108(4), 1234–1239 (2011)CrossRefGoogle Scholar
  68. 76.68
    M. Dorigo, M. Colombetti: Robot shaping: An experiment in behavior engineering (MIT, Cambridge 1997)Google Scholar
  69. 76.69
    J.E. Auerbach, J.C. Bongard: On the relationship between environmental and morphological complexity in evolved robots, Proc. 14th Int. Conf. Genetic Evol. Comput. Conf., ACM (2012) pp. 521–528Google Scholar
  70. 76.70
    J. Hiller, H. Lipson: Automatic design and manufacture of soft robots, IEEE Trans. Robotics 28(2), 457–466 (2012)CrossRefGoogle Scholar
  71. 76.71
  72. 76.72
    J. Bongard, V. Zykov, H. Lipson: Resilient machines through continuous self-modeling, Science 314(5802), 1118–1121 (2006)CrossRefGoogle Scholar
  73. 76.73
    J.C. Bongard: Accelerating self-modeling in cooperative robot teams, IEEE Trans. Evol. Comput. 13(2), 321–332 (2009)CrossRefGoogle Scholar
  74. 76.74
    K.J. Kim, H. Lipson: Towards a theory of mind in simulated robots, Proc. 11th Annual Conf. Companion Genetic Evol. Comput. Conf. Late Break. Pap. ACM (2009) pp. 2071–2076Google Scholar
  75. 76.75
    I. Harvey, P. Husbands, D.T. Cliff, A. Thompson, N. Jakobi: Evolutionary robotics: The Sussex approach, Robotics Auton. Syst. 20, 205–224 (1997)CrossRefGoogle Scholar
  76. 76.76
    P. Husbands, I. Harvey, D. Cliff, G. Miller: Artificial evolution: A new path for AI?, Brain Cogn. 34, 130–159 (1997)CrossRefGoogle Scholar
  77. 76.77
    N. Jakobi: Evolutionary robotics and the radical envelope of noise hypothesis, Adapt. Behav. 6, 325–368 (1998)CrossRefGoogle Scholar
  78. 76.78
    K.O. Stanley, R. Miikkulainen: Evolving neural networks through augmenting topologies, Evol. Comput. 10(2), 99–127 (2002)CrossRefGoogle Scholar
  79. 76.79
    M.A. Arbib: Self-reproducing automata – Some implications for theoretical biology. In: Towards a Theoretical Biology, 2nd edn., ed. by C.H. Waddington (Edinburgh Univ. Press, Edinburgh 1969) pp. 204–226Google Scholar
  80. 76.80
    J. Aloimonos, I. Weiss, A. Bandopadhay: Active vision, Int. J. Comput. Vis. 1(4), 333–356 (1987)CrossRefGoogle Scholar
  81. 76.81
    R. Bajcsy: Active perception, Proc. IEEE 76(8), 996–1005 (1988)CrossRefGoogle Scholar
  82. 76.82
    D.H. Ballard: Animate vision, Artif. Intell. 48(1), 57–86 (1991)CrossRefGoogle Scholar
  83. 76.83
    P.J. Hancock, R.J. Baddeley, L.S. Smith: The principal components of natural images, Network 3, 61–70 (1992)CrossRefGoogle Scholar
  84. 76.84
    D. Floreano, T. Kato, D. Marocco, E. Sauser: Coevolution of active vision and feature selection, Biol. Cybern. 90(3), 218–228 (2004)zbMATHCrossRefGoogle Scholar
  85. 76.85
    D. Floreano, M. Suzuki, C. Mattiussi: Active vision and receptive field development in evolutionary robots, Evol. Comput. 13(4), 527–544 (2005)CrossRefGoogle Scholar
  86. 76.86
    T.D. Sanger: Optimal unsupervised learning in a single-layer feedforward neural network, Neural Netw. 2, 459–473 (1989)CrossRefGoogle Scholar
  87. 76.87
    I. Harvey, E.A. Di Paolo, R. Wood, M. Quinn, E. Tuci: Evolutionary robotics: A new scientific tool for studying cognition, Artif. Life 11(1-2), 79–98 (2005)CrossRefGoogle Scholar
  88. 76.88
    A. Seth: Causal connectivity of evolved neural networks during Behaviour, Netw. Comput. Neural Syst. 16(1), 35–54 (2005)CrossRefGoogle Scholar
  89. 76.89
    E. Izquierdo, S. Lockery: Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans, J. Neurosci. 30, 12908–12917 (2010)CrossRefGoogle Scholar
  90. 76.90
    P. Husbands, R.C. Moioli, Y. Shim, A. Philippides, P.A. Vargas, M. O'Shea: Evolutionary robotics and neuroscience. In: The Horizons of Evolutionary Robotics, ed. by P.A. Vargas, E.A. Di Paolo, I. Harvey, P. Husbands (MIT, Cambridge 2013) pp. 17–64Google Scholar
  91. 76.91
    D.T. Cliff: Computational neuroethology: A provisional manifesto, Proc. 1st Int. Conf. Simul. Adapt. Behav.: Anim. Animat., ed. by J.-A. Meyer, S.W. Wilson (MIT, Cambridge 1991) pp. 29–39Google Scholar
  92. 76.92
    R. Held, A. Hein: Movement-produced stimulation in the development of visually guided behavior, J. Comp. Physiol. Psychol. 56(5), 872–876 (1963)CrossRefGoogle Scholar
  93. 76.93
    R. Held: Plasticity in sensory-motor systems, Sci. Am. 213(5), 84–94 (1965)CrossRefGoogle Scholar
  94. 76.94
    M. Suzuki, D. Floreano, E.A. Di Paolo: The contribution of active body movement to visual development in evolutionary robots, Neural Netw. 18(5/6), 656–665 (2005)CrossRefGoogle Scholar
  95. 76.95
    S. Healy (Ed.): Spatial Representations in Animals (Oxford Univ. Press, Oxford 1998)Google Scholar
  96. 76.96
    N.A. Schmajuk, H.T. Blair: Place learning and the dynamics of spatial navigation: A neural network approach, Adapt. Behav. 1, 353–385 (1993)CrossRefGoogle Scholar
  97. 76.97
    N. Burgess, J.G. Donnett, K.J. Jeffery, J. O'Keefe: Robotic and neuronal simulation of the hippocampus and rat navigation, Philos. Trans. R. Soc. 352, 1535–1543 (1997)CrossRefGoogle Scholar
  98. 76.98
    J. O'Keefe, L. Nadel: The Hippocampus as a Cognitive Map (Clarendon, Oxford 1978)Google Scholar
  99. 76.99
    J.S. Taube, R.U. Muller, J.B. Ranck Jr.: Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis, J. Neurosci. 10, 420–435 (1990)Google Scholar
  100. 76.100
    D.E. Rumelhart, J. McClelland, P.D.P. Group: Parallel Distributed Processing: Explorations in the Microstructure of Cognition (MIT, Cambridge 1986)Google Scholar
  101. 76.101
    W. Maas, C.M. Bishop (Eds.): Pulsed Neural Networks (MIT, Cambridge 1999)Google Scholar
  102. 76.102
    F. Rieke, D. Warland, R. van Steveninck, W. Bialek: Spikes:: Exploring the Neural Code (MIT, Cambridge 1997)zbMATHGoogle Scholar
  103. 76.103
    G. Indiveri, P. Verschure: Autonomous vehicle guidance using analog VLSI neuromorphic sensors, Lect. Notes Comput. Sci. 1327, 811–816 (1997)CrossRefGoogle Scholar
  104. 76.104
    M.A. Lewis, R. Etienne-Cummings, A.H. Cohen, M. Hartmann: Toward biomorphic control using custom aVLSI CPG chips, Proc. IEEE Int. Conf. Robotics Autom. (ICRA) (2000) pp. 494–500Google Scholar
  105. 76.105
    D. Floreano, C. Mattiussi: Evolution of spiking neural controllers for autonomous vision-based robots. In: Evolutionary Robotics. From Intelligent Robotics to Artificial Life, ed. by T. Gomi (Springer, Tokyo 2001) pp. 38–61CrossRefGoogle Scholar
  106. 76.106
    W. Gerstner, J.L. van Hemmen, J.D. Cowan: What matters in neuronal locking?, Neural Comput. 8, 1653–1676 (1996)CrossRefGoogle Scholar
  107. 76.107
    D. Floreano, Y. Epars, J.C. Zufferey, C. Mattiussi: Evolution of spiking neural circuits in autonomous mobile robots, Int. J. Intell. Syst. 21(9), 1005–1024 (2006)CrossRefGoogle Scholar
  108. 76.108
    J.A. Gally, P.R. Montague, G.N. Reeke, G.M. Edelman: The NO hypothesis: Possible effects of a short-lived, rapidly diffusible signal in the development and function of the nervous system, Proc. Natl. Acad. Sci. 87(9), 3547–3551 (1990)CrossRefGoogle Scholar
  109. 76.109
    J. Wood, J. Garthwaite: Models of the diffusional spread of nitric oxide: Implications for neural nitric oxide signaling and its pharmacological properties, Neuropharmacology 33, 1235–1244 (1994)CrossRefGoogle Scholar
  110. 76.110
    T.M. Dawson, S.N. Snyder: Gases as biological messengers: Nitric oxide and carbon monoxide in the brain, J. Neurosci. 14(9), 5147–5159 (1994)Google Scholar
  111. 76.111
    J. Garthwaite, C.L. Boulton: Nitric oxide signaling in the central nervous system, Annu. Rev. Physiol. 57, 683–706 (1995)CrossRefGoogle Scholar
  112. 76.112
    A.O. Philippides, P. Husbands, M. O'Shea: Four-dimensional neuronal signaling by nitric oxide: A computational analysis, J. Neurosci. 20(3), 1199–1207 (2000)Google Scholar
  113. 76.113
    C. Hölscher: Nitric oxide, the enigmatic neuronal messenger: Its role in synaptic plasticity, Trends Neurosci. 20, 298–303 (1997)CrossRefGoogle Scholar
  114. 76.114
    P. Husbands, T. Smith, N. Jakobi, M. O'Shea: Better living through chemistry: Evolving GasNets for robot control, Connect. Sci. 10(4), 185–210 (1998)CrossRefGoogle Scholar
  115. 76.115
    T.M.C. Smith, P. Husbands, M. O'Shea: Local evolvability, neutrality, and search difficulty in evolutionary robotics, Biosystems 69, 223–243 (2003)CrossRefGoogle Scholar
  116. 76.116
    A.O. Philippides, P. Husbands, T. Smith, M. O'Shea: Flexible couplings: Diffusing neuromodulators and adaptive robotics, Artif. Life 11(1-2), 139–160 (2005)CrossRefGoogle Scholar
  117. 76.117
    A.O. Philippides, P. Husbands, T. Smith, M. O'Shea: Structure based models of NO diffusion in the nervous system. In: Computational Neuroscience: A Comprehensive Approach, ed. by J. Feng (CRC, Boca Raton 2004) pp. 97–130Google Scholar
  118. 76.118
    A.O. Philippides, S.R. Ott, P. Husbands, T. Lovick, M. O'Shea: Modeling co-operative volume signaling in a plexus of nitric oxide synthase-expressing neurons, J. Neurosci. 25(28), 6520–6532 (2005)CrossRefGoogle Scholar
  119. 76.119
    P. Husbands, A. Philippides, P. Vargas, C. Buckley, P. Fine, E.A. Di Paolo, M. O'Shea: Spatial, temporal and modulatory factors affecting GasNet evolvability in a visually guided robotics task, Complexity 16(2), 35–44 (2010)CrossRefGoogle Scholar
  120. 76.120
    D. Barañano, C. Ferris, S. Snyder: A typical neural messenger, Trends Neurosci. 24(2), 99–106 (2001)CrossRefGoogle Scholar
  121. 76.121
    T.M.C. Smith, P. Husbands, A. Philippides, M. O'Shea: Neuronal plasticity and temporal adaptivity: Gasnet robot control networks, Adapt. Behav. 10(3/4), 161–184 (2002)CrossRefGoogle Scholar
  122. 76.122
    G. Edelman, J. Gally: Degeneracy and complexity in biological systems, Proc Natl. Acad. Sci. USA 98, 13763–13768 (2001)CrossRefGoogle Scholar
  123. 76.123
    C. Fernando, K. Karishma, E. Szathmáry: Copying and evolution of neuronal topology, PLoS ONE 3(11), e3775 (2008)CrossRefGoogle Scholar
  124. 76.124
    C. Fernando, E. Szathmáry, P. Husbands: Selectionist and evolutionary approaches to brain function: A critical appraisal, Front. Comput. Neurosci. 6, 24 (2012)CrossRefGoogle Scholar
  125. 76.125
    S. Nolfi, D. Floreano: Learning and evolution, Auton. Robots 7, 89–113 (1999)CrossRefGoogle Scholar
  126. 76.126
    S. Nolfi, D. Parisi: Learning to adapt to changing environments in evolving neural networks, Adapt. Behav. 1, 75–98 (1997)Google Scholar
  127. 76.127
    J.M. Baldwin: A new factor in evolution, Am. Nat. 30, 441–451 (1896)CrossRefGoogle Scholar
  128. 76.128
    C.H. Waddington: Canalization of development and the inheritance of acquired characters, Nature 150, 563–565 (1942)CrossRefGoogle Scholar
  129. 76.129
    G. Mayley: Landscapes, learning costs, and genetic assimilation, Evol. Comput. 4, 213–234 (1997)CrossRefGoogle Scholar
  130. 76.130
    D. Floreano, F. Mondada: Evolution of plastic neurocontrollers for situated agents, Proc. 4th Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 4, ed. by P. Maes, M. Matarić, J.A. Meyer, J. Pollack, H. Roitblat, S. Wilson (MIT, Cambridge 1996) pp. 402–410Google Scholar
  131. 76.131
    D. Floreano, J. Urzelai: Evolutionary robots with online self-organization and behavioral fitness, Neural Netw. 13, 431–443 (2000)CrossRefGoogle Scholar
  132. 76.132
    D. Floreano, J. Urzelai: Neural morphogenesis, synaptic plasticity, and evolution, Theory Biosci. 120(3-4), 225–240 (2001)zbMATHCrossRefGoogle Scholar
  133. 76.133
    E. Di Paolo: Evolving spike-timing-dependent plasticity for single-trial learning in robots, Philos. Trans. R. Soc. Lond. 361, 2299–2319 (2003)MathSciNetCrossRefGoogle Scholar
  134. 76.134
    Y.U. Cao, A.S. Fukunaga, A. Kahng: Cooperative mobile robotics: Antecedents and directions, Auton. Robots 4, 7–27 (1997)CrossRefGoogle Scholar
  135. 76.135
    S. Nolfi: Co-evolving predator and prey robots, Adapt. Behav. 20, 10–15 (2012)CrossRefGoogle Scholar
  136. 76.136
    D. Floreano, S. Nolfi: God save the red queen! Competition in co-evolutionary robotics, Proc. 2nd Conf. Genetic Program., ed. by J.R. Koza, K. Deb, M. Dorigo, D. Foegel, B. Garzon, H. Iba, R.L. Riolo (Morgan Kaufmann, San Francisco, CA 1997) pp. 398–406Google Scholar
  137. 76.137
    S. Nolfi, D. Floreano: Co-evolving predator and prey robots: Do arm races arise in artificial evolution?, Artif. Life 4(4), 311–335 (1998)CrossRefGoogle Scholar
  138. 76.138
    D. Floreano, S. Nolfi: Evolution versus design: Controlling autonomous robots, Proc. 4th Eur. Conf. Artif. Life, ed. by P. Husbands, I. Harvey (MIT, Cambridge 1997) pp. 378–387Google Scholar
  139. 76.139
    V. Trianni, S. Nolfi: Evolving collective control, cooperation and distributed cognition. In: Handbook of Collective Robotics – Fundamentals and Challenges, ed. by S. Kernbach (CRC, Boca Raton 2012) pp. 246–276Google Scholar
  140. 76.140
    G. Baldassarre, V. Trianni, M. Bonani, F. Mondada, M. Dorigo, S. Nolfi: Self-organised coordinated motion in groups of physically connected robots, IEEE Trans. Syst. Man Cybern. 37, 224–239 (2007)CrossRefGoogle Scholar
  141. 76.141
    M. Quinn, L. Smith, G. Mayley, P. Husbands: Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors, Philos. Trans. R. Soc. Lond. 361, 2321–2344 (2003)MathSciNetCrossRefGoogle Scholar
  142. 76.142
    G. Baldassarre, D. Parisi, S. Nolfi: Coordination and behavior integration in cooperating simulated robots, Proc. 8th Int. Conf. Simul. Adapt. Behav.: Anim. Animat. 8 (MIT, Cambridge 2003) pp. 385–394Google Scholar
  143. 76.143
    V. Sperati, V. Trianni, S. Nolfi: Self-organised path formation in a swarm of robots, Swarm Intell. 5, 97–119 (2011)CrossRefGoogle Scholar
  144. 76.144
    M. Quinn: Evolving communication without dedicated communication channels, Proc. 6th Eur. Conf. Artif. Life, ed. by J. Kelemen, P. Sosik (Springer, Berlin, Heidelberg 2001) pp. 357–366Google Scholar
  145. 76.145
    D. Marocco, S. Nolfi: Self-organization of communication in evolving robots, Proc. 10th Int. Conf. Artif. Life, ed. by L. Rocha, L. Yeager, M. Bedau, D. Floreano, R. Goldstone, A. Vespignani (MIT, Cambridge 2006) pp. 178–184Google Scholar
  146. 76.146
    D. Floreano, S. Mitri, S. Magnenat, L. Keller: Evolutionary conditions for the emergence of communication in robots, Curr. Biol. 17, 514–519 (2007)CrossRefGoogle Scholar
  147. 76.147
    M. Waibel, D. Floreano, S. Magnenat, L. Keller: Division of labour and colony efficiency in social insects: Effects of interactions between genetic architecture, colony kin structure and rate of perturbations, Proc. Royal Soc. B Biol. Sci. 273, 1815–1823 (2006)CrossRefGoogle Scholar
  148. 76.148
    F. Mondada, G. Pettinaro, A. Guignard, I. Kwee, D. Floreano, J.L. Deneubourg, S. Nolfi, L.M. Gambardella, M. Dorigo: Swarm-bot: A new distributed robotic concept, Auton. Robots 17, 193–221 (2004)CrossRefGoogle Scholar
  149. 76.149
    V. Trianni, S. Nolfi, M. Dorigo: Cooperative hole-avoidance in a swarm-bot, Robotics Auton. Syst. 54, 97–103 (2006)CrossRefGoogle Scholar
  150. 76.150
    G. Baldassarre, S. Nolfi, D. Parisi: Evolving mobile robots able to display collective behavior, Artif. Life 9, 255–267 (2003)CrossRefGoogle Scholar
  151. 76.151
    S. Nolfi: Evolution of communication and language in evolving robots. In: Current Perspective on the origin of language, ed. by C. Lefebvre, B. Comrie, H. Cohen (Cambridge Univ. Press, Cambridge 2013)Google Scholar
  152. 76.152
    J. De Greef, S. Nolfi: Evolution of implicit and explicit communication in a group of mobile robots. In: Evolution of Communication and Language in Embodied Agents, ed. by S. Nolfi, M. Mirolli (Springer, Berlin, Heidelberg 2010) pp. 179–214CrossRefGoogle Scholar
  153. 76.153
    S. Mitri, D. Floreano, L. Keller: The evolution of information suppression in communicating robots with conflicting interests, Proc. Natl. Acad. Sci. 106, 15786–15790 (2009)CrossRefGoogle Scholar
  154. 76.154
    S. Mitri, D. Floreano, L. Keller: Relatedness influences signal reliability in evolving robots, Proc. Royal Soc. B Biol. Sci. 278, 378–383 (2011)CrossRefGoogle Scholar
  155. 76.155
    S. Nolfi: Emergence of communication in embodied agents: Co-adapting communicative and non-communicative behaviours, Connect. Sci. 3-4, 231–248 (2005)CrossRefGoogle Scholar
  156. 76.156
    S. Wischmanna, D. Floreano, L. Keller: Historical contingency affects signaling strategies and competitive abilities in evolving populations of simulated robots, Proc. Natl. Acad. Sci. 109, 864–868 (2011)CrossRefGoogle Scholar
  157. 76.157
    A. Thompson: Evolving electronic robot controllers that exploit hardware resources, Lect. Notes Artif. Intell. 929, 640–656 (1995)Google Scholar
  158. 76.158
    A. Thompson: Hardware Evolution: Automatic Design of Electronic Circuits in Reconfigurable Hardware by Artificial Evolution, Distinguished Dissertation Series (Springer, Berlin, Heidelberg 1998)CrossRefGoogle Scholar
  159. 76.159
    A. Thompson: Artificial evolution in the physical world. In: Evolutionary Robotics. From Intelligent Robots to Artificial Life (ER'97), ed. by T. Gomi (AAI Books, Ottawa 1997) pp. 101–125Google Scholar
  160. 76.160
    D. Keymeulen, M. Durantez, M. Konaka, Y. Kuniyoshi, T. Higuchi: An evolutionary robot navigation system using a gate-level evolvable hardware, Lect. Notes Comput. Sci. 1259, 193–209 (1996)CrossRefGoogle Scholar
  161. 76.161
    G. Ritter, J.-M. Puiatti, E. Sanchez: Leonardo and discipulus simplex: An autonomous, evolvable six-legged walking robot, Lect. Notes Comput. Sci. 1586, 688–696 (1999)CrossRefGoogle Scholar
  162. 76.162
    D. Roggen, D. Floreano, C. Mattiussi: A morphogenetic evolutionary system: Phylogenesis of the POETIC circuit, Lect. Notes Comput. Sci. 2606, 153–164 (2003)zbMATHCrossRefGoogle Scholar
  163. 76.163
    D. Roggen, S. Hofmann, Y. Thoma, D. Floreano: Hardware spiking neural network with run-time reconfigurable connectivity in an autonomous robot, NASA/DoD Conf. Evolv. Hardw., ed. by J. Lohn, R. Zebulum, J. Steincamp, D. Keymeulen, A. Stoica, M.I. Fergusonpages (2003) pp. 189–198Google Scholar
  164. 76.164
    M. Matarić, D. Cliff: Challenges in evolving controllers for physical robots, Robotics Auton. Syst. 19(1), 67–83 (1996)CrossRefGoogle Scholar
  165. 76.165
    G. Massera, T. Ferrauto, O. Gigliotta, S. Nolfi: FARSA: An open software tool for embodied cognitive science, Proc. 12th Eur. Conf. Artif. Life, ed. by P. Lio, O. Miglino, G. Nicosia, S. Nolfi, M. Pavone (MIT, Cambridge 2013) pp. 454–538Google Scholar
  166. 76.166
    Framework for Autonomous Robotics Simulation and Analysis:

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Stefano Nolfi
    • 1
  • Josh Bongard
    • 2
  • Phil Husbands
    • 3
  • Dario Floreano
    • 4
  1. 1.Institute of Cognitive Sciences and TechnologiesNational Research Council (CNR)RomeItaly
  2. 2.Department of Computer ScienceUniversity of VermontBurlingtonUSA
  3. 3.Department of InformaticsUniversity of SussexBrightonUK
  4. 4.Laboratory of Intelligent SystemsSwiss Federal Institute of Technology (EPFL)LausanneSwitzerland

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