Beyond black-box optimization: A review of selective pressures for evolutionary robotics

Evolutionary Intelligence

Volumn 7, Issue 2, 2014, Pages 71-93

  Doncieux, Stephane ^a,b   Mouret, Jean Baptiste ^a,b  

^a SORBONNE UNIVERSITÉ   (France)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Evolutionary robotics; Goal refiner; Process helper; Selective pressures; Task agnostic; Task specific

Indexed keywords

ALGORITHMS; EXPERIMENTS; MACHINE DESIGN; REFINING; ROBOTS;

EVOLUTIONARY ROBOTICS; GOAL REFINER; SELECTIVE PRESSURE; TASK-AGNOSTIC; TASK-SPECIFIC;

OPTIMIZATION;

EID: 84906062898     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-014-0110-x     Document Type: Article

References (189)

1. Alpaydin E (2004) Introduction to machine learning. The MIT Press.
2. Angeline PJ (2000) Competitive fitness evaluation. In: Back T, Fogel DB, Michalewicz Z (eds) Evolutionary computation, vol 2. Taylor & Francis, London, pp 12-14.
3. Auerbach JE, Bongard JC (2009) How robot morphology and training order affect the learning of multiple behaviors. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'2009), pp 39-46.
4. Auerbach JE, Bongard JC (2012) On the relationship between environmental and mechanical complexity in evolved robots. In: Proceedings of artificial life conference (ALife XIII), pp 309-316.
5. Auerbach JE, Bongard JC (2012) On the relationship between environmental and morphological complexity in evolved robots. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'12). ACM Press, New York, NY, USA, pp 521-528.
6. Bajaj D, Ang M (2000) An incremental approach in evolving robot behavior. In: Proceedings of the international conference on control, automation, robotics and vision (ICARCV'2000).
7. Barate R, Manzanera A (2009) Evolution of visual controllers for obstacle avoidance in mobile robotics. Evoluti Intell 2(3): 85-102.
8. Barlow GJ, Oh CK, Grant E (2004) Incremental evolution of autonomous controllers for unmanned aerial vehicles using multi-objective genetic programming. In: Proceedings of IEEE conference on cybernetics and intelligent systems (CIS'2004), vol 2, pp 689-694.
9. Berlanga A, Sanchis A, Isasi P, Molina JM (2000) A general learning co-evolution method to generalize autonomous robot navigation behavior. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'2000), pp 769-776.
10. Berlanga A, Sanchis A, Isasi P, Molina JM (2002) Neural network controller against environment: a coevolutive approach to generalize robot navigation behavior. J Intell Robot Syst 33(2): 139-166.
11. Blanchard P, Devaney RL, Hall GR (2006) Differential equations. Thompson, London.
12. Boeing A, Braunl T (2012) Leveraging multiple simulators for crossing the reality gap. In: Proceedings of international conference on control, automation, robotics and vision (ICARV'2012), pp 1113-1119.
13. Bongard JC (2007) Action-selection and crossover strategies for self-modeling machines. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'07). ACM Press, pp 198-205.
14. Bongard JC (2008) Behavior chaining: incremental behavior integration for evolutionary robotics. In: Proceedings of artificial life conference (ALife XI), pp 64-71.
15. Bongard JC (2009) Accelerating self-modeling in cooperative robot teams. IEEE Trans Evol Comput 13(2): 321-332.
16. Bongard JC (2010) The utility of evolving simulated robot morphology increases with task complexity for object manipulation. Artif Life 16(3): 201-23.
17. Bongard JC (2011) Innocent until proven guilty: reducing robot shaping from polynomial to linear time. IEEE Trans Evol Comput 15(4): 571-585.
18. Bongard JC (2011) Morphological and environmental scaffolding synergize when evolving robot controllers. In: Proceedings of the international conference on genetic and evolutionary computation conference (GECCO'11), pp 179-186.
19. Bongard JC (2011) Morphological change in machines accelerates the evolution of robust behavior. Proc Natl Acad Sci 108(4): 1234-1239.
20. Bongard JC (2013) Evolutionary robotics. Commun ACM 56(08): 74-83.
21. Bongard JC, Hornby GS (2010) Guarding against premature convergence while accelerating evolutionary search. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'10), pp 111-118. ACM.
22. Bongard JC, Hornby GS (2013) Combining fitness-based search and user modeling in evolutionary robotics. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'13). ACM, pp 159-166.
23. Bongard JC, Lipson H (2004) Automated damage diagnosis and recovery for remote robotics. In: Proceedings of the international conference of robotics and automation (ICRA'2004), vol 4:, pp 545-3550.
24. Bongard JC, Lipson H (2004) Automated robot function recovery after unanticipated failure or environmental change using a minimum of hardware trials. In: Proceedings of evolvable hardware, pp 169-176.
25. Bongard JC, Lipson H (2004) Once more unto the breach: co-evolving a robot and its simulator. In: Proceedings of the international conference on the simulation and synthesis of living systems (ALIFE9), pp 57-62.
26. Bongard JC, Zykov V, Lipson H (2006) Resilient machines through continuous self-modeling. Science 314(5802): 1118-1121.
27. Bredeche N, Montanier JM (2010) Environment-driven embodied evolution in a population of autonomous agents. In: Parallel problem solving from nature (PPSN XI). PPSN, vol 6239, pp 290-299.
28. Buason G, Bergfeldt N, Ziemke T (2005) Brains, bodies, and beyond: competitive co-evolution of robot controllers, morphologies and environments. Genet Program Evol Mach 6(1): 25-51.
29. Buason G, Ziemke T (2003) Competitive co-evolution of predator and prey sensory-motor systems. In: Applications of evolutionary computing, pp 605-615.
30. Carroll L (1866) Alice's adventures in wonderland and through the looking glass. MacMillan, New York.
31. Celis S, Hornby GS, Bongard JC (2013) Avoiding local optima with user demonstrations and low-level control. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'2013), pp 3403-3410.
32. Cliff D, Miller GF (1995) Tracking the red queen: measurements of adaptive progress in co-evolutionary simulations. In: Proceedings of the Third European Conference on Artificial Life. LCNS vol 929, pp 200-218.
33. Cliff D, Miller GF (1996) Co-evolution of pursuit and evasion II: simulation methods and results. In: Proceedings of the international conference on simulation of adaptive behavior (SAB'96).
34. Clune J, Lipson H (2011) Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In: Proceedings of the European conference on artificial life (ECAL'11).
35. Clune J, Stanley KO, Pennock RT, Ofria C (2011) On the performance of indirect encoding across the continuum of regularity. IEEE Trans Evol Comput 15(3): 346-367.
36. Cuccu G, Gomez F (2011) When novelty is not enough. In: Applications of evolutionary computation, pp 234-243.
37. Cully A, Mouret J-B (2013) Behavioral repertoire learning in robotics. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'13), pp 175-182.
38. Dawkins R (1976) The selfish gene. Oxford University Press, Oxford.
39. Dawkins R, Krebs JR (1979) Arms races between and within species. Proc R Soc B Biol Sci 205(1161): 489-511.
40. De Garis H (1990) Building nanobrains with genetically programmed neural networks modules. In: Proceedings of the international joint conference on neural networks (IJCNN'1990), pp 511-516.
41. De Jong ED, Pollack JB (2004) Ideal evaluation from coevolution. Evol Comput 12(2): 159-192.
42. De Jong ED, Watson RA, Pollack JB (2001) Reducing bloat and promoting diversity using multi-objective methods. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'01), pp 11-18. ACM.
43. De Jong KA (2006) Evolutionary computation: a unified approach, vol 262041944. MIT Press, Cambridge.
44. de Nardi R, Holland OE (2008) Coevolutionary modelling of a miniature rotorcraft. In: Proceedings of the international conference on intelligent autonomous systems (IAS10).
45. de Nardi R, Togelius J, Holland OE, Lucas SM (2006) Evolution of neural networks for helicopter control: why modularity matters. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'2006), pp 1799-1806. IEEE.
46. Deb K (2001) Multi-objectives optimization using evolutionnary algorithms. Wiley, London.
47. Delarboulas P, Schoenauer M, Sebag M (2010) Open-ended evolutionary robotics: an information theoretic approach. In: Proceedings of parallel problem solving from nature (PPSN XI), vol 216342, pp 334-343.
48. Di Mario E, Navarro I, Martinoli A (2013) The effect of the environment in the synthesis of robotic controllers: a case study in multi-robot obstacle avoidance using distributed particle swarm optimization. In: Advances in artificial life, ECAL 2013, Sept 2013, pp 561-568.
49. Doncieux S (2013) Transfer learning for direct policy search: a reward shaping approach. In: Proceedings of the IEEE conference on development and learning and epigenetic robotics (ICDL-EpiRob 2013).
50. Doncieux S, Meyer J-A (2004) Evolving modular neural networks to solve challenging control problems. In: Proceedings of the fourth international ICSC symposium on engineering of intelligent systems (EIS 2004).
51. Doncieux S, Mouret J-B (2010) Behavioral diversity measures for evolutionary robotics. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2010), pp 1303-1310.
52. Doncieux S, Mouret J-B (2013) Behavioral diversity with multiple behavioral distances. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2013), June 2013, pp 1427-1434. IEEE.
53. Doncieux S, Mouret J-B, Bredeche N, Padois V (2011) Evolutionary robotics: exploring new horizons. Springer, Berlin, pp 3-25.
54. Dozier G (2001) Evolving robot behavior via interactive evolutionary computation: from real-world to simulation. In: Proceedings of the ACM symposium on applied computing (SAC'2001), pp 340-344. ACM.
55. Duarte M, Oliveira S, Christensen AL (2012) Hierarchical evolution of robotic controllers for complex tasks. In: Proceedings of the IEEE conference on development and learning and epigenetic robotics (ICDL-EpiRob 2012).
56. Eiben AE, Smith JE (2008) Introduction to evolutionary computing (natural computing series). Springer, Berlin.
57. Farchy A, Barrett S, MacAlpine P, Stone P (2013) Humanoid robots learning to walk faster: from the real world to simulation and back. In: Proceedings of the international conference on autonomous agents and multi-agent systems (AAMAS'2013), pp 39-46.
58. Filliat D, Kodjabachian J, Meyer J-A (1999) Evolution of neural controllers for locomotion and obstacle-avoidance in a 6-legged robot. Connect Sci 11: 223-240.
59. Floreano D, Dürr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1): 47-62.
60. Floreano D, Mattiussi C (2008) Bio-inspired artificial intelligence: theories, methods, and technologies. Intelligent robotics and autonomous agents. MIT Press, Cambridge.
61. Floreano D, Mondada F (1998) Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks 11(7-8): 1461-1478.
62. Floreano D, Nolfi S (1997) Adaptive behavior in competing co-evolving species. In: Proceedings of the European conference on artificial life (ECAL'97), pp 378-387.
63. Floreano D, Nolfi S (1997) God save the red queen! Competition in co-evolutionary robotics. In: Proceedings of the 2nd conference on genetic programming, vol 5.
64. Floreano D, Nolfi S, Mondada F (1998) Competitive co-evolutionary robotics: from theory to practice. In: Proceedings of the international conference on simulation of adaptive behavior (SAB98), pp 515-524.
65. Floreano D, Nolfi S, Mondada F (2001) Co-evolution and ontogenetic change in competing robots. In: Advances in the evolutionary synthesis of intelligent agents, pp 273-306.
66. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York.
67. Friedrich T, Oliveto PS, Sudholt D, Witt C (2008) Theoretical analysis of diversity mechanisms for global exploration. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'08), pp 945-952. ACM.
68. Goldberg DE (1987) Simple genetic algorithms and the minimal, deceptive problem. In: Davis L (eds) Genetic algorithms and simulated annealing. Morgan Kaufman, San Mato, pp 74-88.
69. Gomes J, Christensen AL (2013) Generic behaviour similarity measures for evolutionary swarm robotics. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO13), pp 199-206.
70. Gomes J, Urbano P, Christensen AL (2012) Introducing novelty search in evolutionary swarm robotics. In: Proceedings of the international conference on swarm intelligence (ANTS'2012), pp 85-96.
71. Gomes J, Urbano P, Christensen AL (2012) Progressive minimal criteria novelty search. In: Advances in artificial intelligence (IBERAMIA), pp 281-290.
72. Gomes J, Urbano P, Christensen AL (2013) Evolution of swarm robotics systems with novelty search. Swarm Intell 7(2-3): 115-144.
73. Gomez FJ (2009) Sustaining diversity using behavioral information distance. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO09), pp 113-120. ACM.
74. Gomez FJ, Miikkulainen R (1997) Incremental evolution of complex general behavior. Adapt Behav 5(3-4): 317-342.
75. Gomez FJ, Miikkulainen R (2004) Transfer of neuroevolved controllers in unstable domains. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO04), pp 957-968.
76. Gould SJ, Vrba ES (1982) Exaptation-a missing term in the science of form. Paleobiology 8(1): 4-15.
77. Grefenstette J, Daley R (1996) Methods for competitive and cooperative co-evolution. In: Adaptation, coevolution and learning in multiagent systems: papers from the 1996 AAAI Spring Symposium.
78. Gruau F, Quatramaran K (1997) Cellular encoding for interactive evolutionary robotics. In: Proceedings of European conference on artificial life (ECAL'97), pp 368-377.
79. Haasdijk E, Weel B, Eiben A (2013) Right on the monee. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO13), pp 207-214.
80. Hartland C, Bredeche N, Sebag M (2009) Memory-enhanced evolutionary robotics: the echo state network approach. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'2009), pp 2788-2795.
81. Harvey I, Husbands P, Cliff D (1994) Seeing the light: artificial evolution; real vision. In: Cliff D, Husbands P, Meyer J-A, Wilson S (eds) Proceedings of the international conference on simulation of adaptive behavior (SAB94). MIT Press/Bradford Books, Cambridge, pp 392-401.
82. Hillis WD (1990) Co-evolving parasites improve simulated evolution as an optimization procedure. Phys D Nonlinear Phenom 42(1): 228-234.
83. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
84. Hornby GS (2009) Steady-state ALPS for real-valued problems. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'09), pp 795-802, New York, NY, USA. ACM Press.
85. Hornby GS, Pollack JB (2002) Creating high-level components with a generative representation for body-brain evolution. Artif Life 8(3): 223-246.
86. Hornby GS (2006) ALPS: the age-layered population structure for reducing the problem of premature convergence. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO'06), pp 815-822.
87. Hsu WH, Gustafson SM (2002) Genetic programming and multi-agent layered learning by reinforcements. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO02), pp 764-771.
88. Jakobi N (1997) Evolutionary robotics and the radical envelope of noise hypothesis. Adapt Behav 6(1): 131-174.
89. Jakobi N, Husbands P, Harvey I (1995) Noise and the reality gap: the use of simulation in evolutionary robotics. In: Lecture notes in computer science, vol 929, pp 704-720.
90. Jensen MT (2004) Helper-objectives: using multi-objective evolutionary algorithms for single-objective optimisation. J Math Model Algorithms 3(4): 323-347.
91. Klyubin AS, Polani D, Nehaniv CL (2005) Empowerment: a universal agent-centric measure of control. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp 128-135.
92. Knowles J, Watson Richard A, Corne D (2001) Reducing local optima in single-objective problems by multi-objectivization. In Evolutionary multi-criterion optimization, pp 269-283. Springer.
93. Knowles JD, Watson RA, Corne DW (2001) Reducing local optima in single-objective problems by multi-objectivization. In: Proceedings of first international conference on evolutionary multi-criterion optimization 1993, pp 268-282.
94. Chavas J, Corne C, Horvai P, Kodjabachian J, Meyer JA (1998) Incremental evolution of neural controllers for robust obstacle-avoidance in Khepera. In: Husbands P, Meyer JA (eds) Proceedings of the first European workshop on evolutionary robotics - EvoRobot'98. LCNS vol 1468. Springer, pp 227-247.
95. Kodjabachian J, Meyer J-A (1997) Evolution and development of neural networks controlling locomotion, gradient-following, and obstacle-avoidance in artificial insects. IEEE Trans Neural Netw 9: 796-812.
96. Koos S, Mouret J-B, Doncieux S (2009) Automatic system identification based on coevolution of models and tests. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2009), pp 560-567.
97. Koos S, Mouret J-B, Doncieux S (2010) Crossing the reality gap in evolutionary robotics by promoting transferable controllers. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO10), pp 119-126.
98. Koos S, Mouret J-B, Doncieux S (2013) The transferability spproach: crossing the reality gap in evolutionary robotics. IEEE Trans Evol Comput 17(1): 122-145.
99. Koza JR (1993) Genetic programming: on the programming of computers by means of natural selection. MIT Press, London.
100. Krcah P (2010) Solving deceptive tasks in robot body-brain co-evolution by searching for behavioral novelty. In: Proceedings of the international conference on intelligent systems design and applications (ISDA'2010), pp 284-289.
101. Kuhn TS (1962) The structure of scientific revolutions. University of Chicago Press, Chicago.
102. Lee W (1999) Evolving complex robot behaviors. Inf Sci 121(1-2): 1-25.
103. Lehman J, Risi S, Ambrosio DD, Stanley KO (2013) Encouraging reactivity to create robust machines. Adapt Behav 21: 484-500.
104. Lehman J, Stanley KO (2008) Exploiting open-endedness to solve problems through the search for novelty. In: Proceedings of artificial life conference (ALife XI), pp 329-336.
105. Lehman J, Stanley KO (2010) Revising the evolutionary computation abstraction: minimal criteria novelty search. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO10), pp 103-110.
106. Lehman J, Stanley KO (2011) Abandoning objectives: evolution through the search for novelty alone. Evol Comput 19(2): 189-223.
107. Lehman J, Stanley KO (2011) Evolving a diversity of creatures through novelty search and local competition. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO11), pp 211-218.
108. Lehman J, Stanley KO (2011) Novelty search and the problem with objectives. Genet Program Theory Pract IX, pp 37-56.
109. Lehman J, Stanley KO (2013) Evolvability is inevitable: increasing evolvability without the pressure to adapt. PloS One 8(4): e62186.
110. Lehman J, Stanley KO, Miikkulainen R (2013) Effective diversity maintenance in deceptive domains. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO13). ACM Press, New York, NY, USA, pp 215-222.
111. Lewis MA, Fagg AH, Solidum A (1992) Genetic programming approach to the construction of a neural network for control of a walking robot. In: Proceedings of the IEEE international conference on robotics and automation (ICRA'1992), pp 2618-2623.
112. Liapis A, Yannakakis GN, Togelius Julian (2013) Enhancements to constrained novelty search: two-population novelty search for generating game content. In: Proceedings of of the international conference on genetic and evolutionary computation (GECCO'13), pp 343-350.
113. Lipson H (2005) Evolutionary robotics and open-ended design automation. Biomimetics 17(9): 129-155.
114. Lipson H, Pollack JB (2000) Automatic design and manufacture of robotic lifeforms. Nature 406: 974-978.
115. Lund HH, Miglino O (1998) Evolving and breeding robots. Evol Robot, LCNS vol 1468. Springer, pp 192-210.
116. Lund HH, Miglino O, Pagliarini L, Billard A, Ijspeert A (1998) Evolutionary robotics-a children's game. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC1998), pp 154-158. IEEE.
117. Mahfoud SW (1997) Niching methods. In: Bäck T, Fogel DB, Michalewicz Z (eds) Handbook of evolutionary computation. Taylor & Francis, London.
118. Meyer J-A, Guillot A (2008) Biologically-inspired robots. In: Siciliano O, Khatib B (eds) Handbook of robotics. Springer, Berlin, pp 1-38.
119. Meyer J-A, Guillot A, Girard B, Khamassi M, Pirim P, Berthoz A (2005) The Psikharpax project: towards building an artificial rat. Robot Auton Syst 50(4): 211-223.
120. Meyer J-A, Wilson S (1991) Simulation of adaptive behavior in animats: review and prospect. In: Proceedings of the international conference on simulation of adaptive behavior (SAB91), pp 2-14.
121. Miglino O, Lund HH, Nolfi S (1995) Evolving mobile robots in simulated and real environments. Artif Life 2(4): 417-434.
122. Miller GF, Cliff D (1994) Protean behavior in dynamic games: arguments for the co-evolution of pursuit-evasion tactics. In: Proceedings of the international conference on simulation of adaptive behavior (SAB94), pp 411-420. MIT Press.
123. Moriarty DE, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evol Comput 5(4): 373-399.
124. Moriguchi H, Honiden S (2010) Sustaining behavioral diversity in NEAT. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO10), pp 611-618. ACM.
125. Moshaiov A, Ashram-Wittenberg A (2009) Multi-objective evolution of robot neuro-controllers. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2009), pp 1093-1100.
126. Mouret J-B (2011) Novelty-based multiobjectivization. In New horizons in evolutionary robotics: extended contributions of the 2009 EvoDeRob workshop, pp 139-154. Springer.
127. Mouret J-B, Doncieux S (2008) Incremental evolution of animats' behaviors as a multi-objective optimization. In: Proceedings of the international conference on simulation of adaptive behavior (SAB08), vol 5040, pp 210-219. Springer.
128. Mouret J-B, Doncieux S (2008) MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars. Evol Intell 1: 187-207.
129. Mouret J-B, Doncieux S (2009) Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2009), pp 1161-1168.
130. Mouret J-B, Doncieux S (2009) Using behavioral exploration objectives to solve deceptive problems in neuro-evolution. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO09), pp 627-634. ACM.
131. Mouret J-B, Doncieux S (2012) Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evol Comput 20(1): 91-133.
132. Mouret J-B, Doncieux S, Meyer J-A (2006) Incremental evolution of target-following neuro-controllers for flapping-wing animats. In: Proceedings of the international conference on simulation of adaptive behavior (SAB06), pp 606-618.
133. Mouret J-B, Koos S, Doncieux S (2012) Crossing the reality gap: a short introduction to the transferability approach. In: Proceedings of the ALIFE workshop "evolution in physical systems".
134. Nelson AL, Barlow GJ, Doitsidis L (2009) Fitness functions in evolutionary robotics: a survey and analysis. Robot Auton Syst 57(4): 345-370.
135. Nelson AL, Grant E, Henderson TC (2004) Evolution of neural controllers for competitive game playing with teams of mobile robots. Robot Auton Syst 46(3): 135-150.
136. Nitschke G (2003) Co-evolution of cooperation in a pursuit evasion game. In: Procedings of IEEE/RSJ international conference on intelligent robots and systems (IROS 2003) 2: 2037-2042.
137. Nojima Y, Kojima F, Kubota N (2003) Trajectory generation for human-friendly behavior of partner robot using fuzzy evaluating interactive genetic algorithm. In: Proceedings of the IEEE international symposium on computational intelligence in robotics and automation. Computational intelligence in robotics and automation for the new millennium, vol 1, pp 306-311. IEEE.
138. Nolfi S (1997) Evolving non-trivial behaviors on real robots: a garbage collecting robot. Robot Auton Syst 22(3-4): 187-198.
139. Nolfi S (2011) Co-evolving predator and prey robots. Adapt Behav 20(1): 10-15.
140. Nolfi S, Floreano D (1998) How co-evolution can enhance the adaptive power of artificial evolution: implications for evolutionary robotics. In: Proceedings of the first European workshop on evolutionary robotics (EvoRobot98), pp 22-38.
141. Nolfi S, Floreano D (2001) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. Bradford Book, Cambridge.
142. Oliveira MAC, Doncieux S, Mouret J-B, Peixoto dos Santos CM (2013) Optimization of humanoid walking controller: crossing the reality gap. In: Proceedings of the IEEE-RAS international conference on humanoid robots (Humanoids'2013).
143. Oliveira MAC, Santos CP (2011) Multi-objective parameter CPG optimization for gait generation of a quadruped robot considering behavioral diversity. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS'2011), Sept 2011, pp 2286-2291. IEEE.
144. Ollion C (2013) Emergence of internal representations in evolutionary robotics: influence of multiple selective pressures. PhD thesis, Pierre and Marie Curie University.
145. Ollion C, Doncieux S (2011) Why and how to measure exploration in behavioral space. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO11), pp 267-274.
146. Ollion C, Doncieux S (2012) Towards behavioral consistency in neuroevolution. In: Proceedings of the international conference on simulation of adaptive behavior (SAB12), pp 177-186.
147. Ollion C, Pinville T, Doncieux S (2012) With a little help from selection pressures: evolution of memory in robot controllers. In: Proceedings of artificial life conference (ALife XIII), pp 407-414.
148. Ostergaard EH, Lund HH (2003) Co-evolving complex robot behavior. From biology to hardware. In: Evolvable systems, pp 308-319.
149. Oudeyer P-Y, Kaplan F, Hafner VV (2007) Intrinsic motivation systems for autonomous mental development. IEEE Trans Evol Comput 11(2): 265-286.
150. Paredis J (2000) Coevolutionary algorithms. In: Evolutionary computation, vol 2. Taylor & Francis, London, pp 224-238.
151. Parker GB (2001) The incremental evolution of gaits for hexapod robots. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO01), pp 1114-1121.
152. Pfeifer R, Bongard JC (2006) How the body shapes the way we think. MIT Press, London.
153. Pfeifer R, Lungarella M, Iida F (2007) Self-organization, embodiment, and biologically inspired robotics. Science 318(5853): 1088-93.
154. Pinville T, Koos S, Mouret J-B, Doncieux S (2011) How to promote generalisation in evolutionary robotics: the ProGAb approach formalising the generalisation ability. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO11), pp 259-266.
155. Prokopenko M, Gerasimov V, Tanev I (2006) Evolving spatiotemporal coordination in a modular robotic system. In: Proceedings of the international conference on simulation of adaptive behavior (SAB06), pp 558-569.
156. Risi S, Vanderbleek SD, Hughes CE, Stanley KO (2009) How novelty search escapes the deceptive trap of learning to learn. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO09), pp 153-160. ACM.
157. Roberts RM (1989) Serendipity: accidental discoveries in science. Wiley Science Editions, London.
158. Sakamoto K, Zhao Q (2006) A study on generating good environment patterns for evolving robot navigators. In: Proceedings of the IEEE international conference on systems, man and cybernetics, vol 4, pp 3280-3285, Oct 2006. IEEE.
159. Sareni B, Krahenbuhl L (1998) Fitness sharing and niching methods revisited. IEEE Trans Evol Comput 2(3): 97-106.
160. Schaul T, Sun Y, Wierstra D, Gomez F, Schmidhuber J (2011) Curiosity-driven optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2011), pp 1343-1349.
161. Schmidt M, Lipson H (2010) Age-fitness pareto optimization. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO10), pp 543-544.
162. Schwefel H-P (1977) Numerische Optimierung von Computer Modellen mittels der Evolutionsstrategie. Birkhäuser, Basel.
163. Secretan J, Beato N, D'Ambrosio DB, Rodriguez A, Campbell A, Folsom-Kovarik JT, Stanley KO (2011) Picbreeder: a case study in collaborative evolutionary exploration of design space. Evol Comput 19(3): 373-403.
164. Siciliano O, Khatib B (2008) Handbook of robotics. Springer, Berlin.
165. Sims K (1994) Evolving virtual creatures. In: Proceedings of SIGGRAPH '94, pp 15-22, New York, NY, USA. ACM Press.
166. Smith T, Husbands P, O'Shea M (2001) Neutral networks in an evolutionary robotics search space. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC2001), vol 1, pp 136-143.
167. Sperati V, Trianni V, Nolfi S (2008) Evolving coordinated group behaviours through maximisation of mean mutual information. Swarm Intell 2(2-4): 73-95.
168. Sporns O, Lungarella M (2006) Evolving coordinated behavior by maximizing information structure. In: Proceedings of the Artificial Life Conference (ALIFE X), pp 323-329.
169. Stanley KO, D'Ambrosio DB, Gauci J (2009) A hypercube-based indirect encoding for evolving large-scale neural networks. Artif Life 15(2): 185-212.
170. Stanley KO, Miikkulainen R (2002) Continual coevolution through complexification. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO02), pp 113-120.
171. Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2): 99-127.
172. Stanley KO, Miikkulainen R (2004) Competitive coevolution through evolutionary complexification. J Artif Intell Res 21: 63-100.
173. Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9): 1275-1296.
174. Toffolo A, Benini E (2003) Genetic diversity as an objective in multi-objective evolutionary algorithms. Evoluti Comput 11(2): 151-167.
175. Trujillo L, Olague G, Lutton E, De Vega FF (2008) Behavior-based speciation for evolutionary robotics. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO08), pp 297-298, New York, NY, USA. ACM.
176. Trujillo L, Olague G, Lutton E, De Vega FF (2008) Discovering several robot behaviors through speciation. In: Application of evolutionary computing: 4th European workshop on bio-inspired heuristics for design automation, pp 165-174. Springer.
177. Trujillo L, Olague G, Lutton E, Dozal L, Clemente E (2011) Speciation in behavioral space for evolutionary robotics. J Intell ZZ Robot Syst 64(3): 323-351.
178. Uchibe E, Nakamura M, Asada M (1999) Cooperative and competitive behavior acquisition for mobile robots through co-evolution. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO99), vol 1, pp 425-430.
179. Urzelai J, Floreano D (1999) Incremental evolution with minimal resources. In: Proceedings of IKW99, pp 796-803.
180. Urzelai J, Floreano D, Dorigo M, Colombetti M (1998) Incremental robot shaping. Connect Sci 10(3): 341-360.
181. van Valen L (1973) Body size and numbers of plants and animals. Evolution 27(1): 27-35.
182. Watson RA, Ficici SG, Pollack JB (2002) Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot Auton Syst 39: 1-18.
183. Whiteson S, Kohl N, Miikkulainen R, Stone P (2005) Evolving soccer keepaway players through task decomposition. Mach Learn 59(1): 5-30.
184. Winkeler JF, Manjunath BS (1998) Incremental evolution in genetic programming. In: Proceedings of the third annual conference on genetic programming, pp 403-411.
185. Woolley BG, Stanley KO (2011) On the deleterious effects of a priori objectives on evolution and representation. In: Proceedings of the international conference on genetic and evolutionary computation (GECCO11). ACM Press, New York, NY, USA, pp 957-964.
186. Woolley BG, Stanley KO (2014) A novel human-computer collaboration: combining novelty search with interactive evolution. In: Proceedings of GECCO'2014, pp 1-8.
187. Zagal JC, Delpiano J, Ruiz-del Solar J (2009) Self-modeling in humanoid soccer robots. Robot Auton Syst 57(8): 819-827.
188. Zagal JC, Ruiz-del Solar J (2007) Combining simulation and reality in evolutionary robotics. J Intell Robot Syst 50(1): 19-39.
189. Zagal JC, Ruiz-del Solar J, Vallejos P (2004) Back to reality: crossing the reality gap in evolutionary robotics. In: Proceedings of IAV.