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Aggregate Selection in Evolutionary Robotics

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Mobile Robots: The Evolutionary Approach

Part of the book series: Studies in Computational Intelligence ((SCI,volume 50))

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References

  1. Filliat D, Kodjabachian J, Meyer J A (1999) Incremental evolution of neural controllers for navigation in a 6 1egged robot. In: Sugisaka, Tanaka (eds) Proc. Fourth International Symposium on Artificial Life and Robotics. Oita Univ. Press

    Google Scholar 

  2. Lund H H, Miglino O, Pagliarini L, Billard A, Ijspeert A (1998) Evolutionary robotics - a children’s game. In: Evolutionary Computation Proceedings, 1998 IEEE World Congress on Computational Intelligence

    Google Scholar 

  3. Lund H H, Miglino O (1996) From simulated to real robots. In: Proceedings of IEEE International Conference on Evolutionary Computation

    Google Scholar 

  4. Banzhaf W, Nordin P, Olmer M (1997) Generating adaptive behavior using function regression within genetic programming and a real robot. In: Proceed-ings of the Second International Conference on Genetic Programming. San Francisco

    Google Scholar 

  5. Jakobi N (1998) Running across the reality gap: Octopod locomotion evolved in a minimal simulation. In: Husbands P, Meyer J A (eds) Evolutionary Robotics: First European Workshop. EvoRobot98. Springer-Verlag

    Google Scholar 

  6. Kawai K, Ishiguro A, Eggenberger P (2001) Incremental evolution of neurocon-trollers with a diffusion-reaction mechanism of neuromodulators. In: Proceed-ings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’01), vol. 4. Maui, HI

    Google Scholar 

  7. Hornby G S, Takamura S, Yokono J, Hanagata O, Fujita M, Pollack J (2000) Evolution of controllers from a high-level simulator to a high dof robot. In: Miller J (ed) Evolvable Systems: from Biology to Hardware; Proceedings of the Third International Conference (ICES 2000). Lecture Notes in Computer Science, vol. 1801. Springer

    Google Scholar 

  8. Lipson H, Pollack J B (2000) Automatic design and manufacture of robotic lifeforms. Nature 406(6799):974-978

    Article  Google Scholar 

  9. Hoffmann F, Zagal Montealegre J C S (2001) Evolution of a tactile wall-following behavior in real time. In: The 6th Online World Conference on Soft Computing in Industrial Applications (WSC6)

    Google Scholar 

  10. Schultz A C, Grefenstette J J, Adams W (1996) RoboShepherd: learning a complex behavior. In: Robotics and Manufacturing: Recent Trends in Research and Applications, vol. 6

    Google Scholar 

  11. Keymeulen D, Iwata M, Kuniyoshi Y, Higuchi T (1998) Online evolution for a self-adapting robotic navigation system using evolvable hardware. Artificial Life 4(4):359-393

    Article  Google Scholar 

  12. Quinn M, Smith L, Mayley G, Husbands P (2002) Evolving team behaviour for real robots. In: EPSRC/BBSRC International Workshop on Biologically-Inspired Robotics: The Legacy of W. Grey Walter (WGW ’02). HP Bristol Labs, U.K.

    Google Scholar 

  13. Nolfi S, Floreano D (1998) Co-evolving predator and prey robots: Do ‘arms races’ arise in artificial evolution? Artificial Life 4(4):311-335

    Article  Google Scholar 

  14. Buason G, Bergfeldt N, Ziemke T (2005) Brains, bodies, and beyond: competi-tive co-evolution of robot controllers, morphologies and environments. Genetic Programming and Evolvable Machines 6(1):25-51

    Article  Google Scholar 

  15. Cliff D, Miller G F (1996) Co-evolution of pursuit and evasion II: simulation methods and results. In: Maes P, Mataric M, Meyer J-A, Pollack J, Wilson S W (eds) From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB96). MIT Press/Bradford Books

    Google Scholar 

  16. Chellapilla K, Fogel D B (2001) Evolving an expert checkers playing program without using human expertise. IEEE Transactions on Evolutionary Computa-tion 5(4):422-428

    Article  Google Scholar 

  17. Lubberts A, Miikkulainen R (2001) Co-evolving a go-playing neural network. In: Coevolution: Turning Algorithms upon Themselves, Birds-of-a-Feather Work-shop, Genetic and Evolutionary Computation Conference (GECCO-2001). San Francisco

    Google Scholar 

  18. Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, Cambridge, MA

    Google Scholar 

  19. Harvey I, Husbands P, Cliff D, Thompson A, Jakobi N (1997) Evolutionary robotics: the Sussex approach. Robotics and Autonomous Systems 20(2-4):205-224

    Article  Google Scholar 

  20. Meeden L A, Kumar D (1998) Trends in evolutionary robotics. In: Jain L C, Fukuda T (eds) Soft Computing for Intelligent Robotic Systems. Physica-Verlag, New York

    Google Scholar 

  21. 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) From Animals to Animates 3. Proc. of 3rd Intl. Conf. on Simulation of Adaptive Behavior (SAB94). MIT Press/Bradford Books, Cambridge, MA

    Google Scholar 

  22. Watson R A, Ficici S G, Pollack J B (2002) Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robotics and Autonomous Systems 39(1):1-18

    Article  Google Scholar 

  23. Kodjabachian J, Meyer J-A (1998) Evolution and development of neural net-works controlling locomotion, gradient-following, and obstacle avoidance in artificial insects. IEEE Transaction on Neural Networks 9(5):796-812

    Article  Google Scholar 

  24. Floreano D, Mondada F (1996) Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man, Cybernetics Part B: Cybernetics 26(3):396-407

    Article  Google Scholar 

  25. Beer R D, Gallagher J C (1992) Evolving dynamical neural networks for adap-tive behavior. Adaptive Behavior 1(1):91-122

    Article  Google Scholar 

  26. Grefenstette J, Schultz A (1994) An evolutionary approach to learning in robots. Machine Learning Workshop on Robot Learning. New Brunswick

    Google Scholar 

  27. Augustsson P, Wolff K, Nordin P (2002) Creation of a learning, flying robot by means of evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002). New York

    Google Scholar 

  28. Brooks R A (1992) Artificial life and real robots. In: Varela F J, Bourgine P (eds) Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life. MIT Press/Bradford Books, Cam-bridge, MA

    Google Scholar 

  29. Brooks R A (1990) Elephants don’t play chess. Robotics and Autonomous Systems 6:3-15

    Article  Google Scholar 

  30. Hornby G S, Lipson H, Pollack J B (2001) Evolution of generative design sys-tems for modular physical robots. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’01), vol. 4

    Google Scholar 

  31. Capi G, Doya K (2005) Evolution of recurrent neural controllers using an extended parallel genetic algorithm. Robotics and Autonomous Systems 52(2-3):148-159

    Article  Google Scholar 

  32. Nelson A L, Grant E, Barlow G J, White M (2003) Evolution of autonomous robot behaviors using relative competitive fitness. In: Proceedings of the 2003 IEEE International Conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS’03). Modeling, Exploration, and Engineering Systems. Boston, MA

    Google Scholar 

  33. Koza J R (1992) Evolution of subsumption using genetic programming. In: Varela F J, Bourgine P (eds) Toward a Practice of Autonomous Sys-tems: Proceedings of the First European Conference on Artificial Life. MIT Press/Bradford Books, Cambridge, MA

    Google Scholar 

  34. Ishiguro A, Tokura S, Kondo T, Uchikawa Y (1999) Reduction of the gap between simulated and real environments in evolutionary robotics: a dynamically-rearranging neural network approach. In: Proceedings of the 1999 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3

    Google Scholar 

  35. Lee W, Hallam J, Lund H H (1996) A hybrid GP/GA approach for co-evolving controllers and robot bodies to achieve fitness-specified task. In: Proceedings of IEEE 3rd International Conference on Evolutionary Computation

    Google Scholar 

  36. Cliff D, Miller G F (1995) Tracking the red queen: measurements of adaptive progress in co-evolutionary simulations. In: Moran F, Moreno A, Merelo J J, Cachon P (eds) Proceedings of the Third European Conference on Artificial Life: Advances in Artificial Life (ECAL95). Lecture Notes in Artificial Intelli-gence 929. Springer-Verlag

    Google Scholar 

  37. Nolfi S (1997) Evolving non-trivial behaviors on real robots. Robotics and Autonomous Systems 22(3-4):187-198

    Article  Google Scholar 

  38. Ziemke T (1999) Remembering how to behave: recurrent neural networks for adaptive robot behavior. In: Medsker, Jain (eds), Recurrent Neural Networks: Design and Applications. CRC Press, Boca Raton

    Google Scholar 

  39. Floreano D, Urzelai J (2000) Evolutionary robots with on-line self-organization and behavioral fitness. Neural Networks 13(4-5):431-443

    Article  Google Scholar 

  40. Tuci E, Quinn M, Harvey I (2002) Evolving fixed-weight networks for learning robots. In: Proceedings of the 2002 Congress on Evolutionary Computing, vol. 2. Honolulu, HI

    Google Scholar 

  41. Ashiru I, Czarnecki C A (1998) Evolving communicating controllers for mul-tiple mobile robot systems. In: Proceedings of the 1998 IEEE International Conference on Robotics and Automation, vol. 4

    Google Scholar 

  42. Quinn M (2000) Evolving cooperative homogeneous multi-robot teams. In: Pro-ceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’00), vol. 3. Takamatsu, Japan

    Google Scholar 

  43. Baldassarre G, Nolfi S, Parisi D (2002) Evolving mobile robots able to display collective behaviors. In: Hemelrijk C K, Bonabeau E (eds) Proceedings of the International Workshop on Self-Organisation and Evolution of Social Behav-iour. Monte Verit, Ascona, Switzerland

    Google Scholar 

  44. Dorigo M, Trianni V, Sahin E, Labella T, Grossy R, Baldassarre G, Nolfi S, Deneubourg J-L, Mondada F, Floreano D, Gambardella L (2004) Evolving self-organizing behaviors for a swarm-bot. Autonomous Robots 17(23):223-245

    Article  Google Scholar 

  45. Nordin P, Banzhaf W, Brameier M (1998) Evolution of a world model for a miniature robot using genetic programming. Robotics and Autonomous Systems 25(1-2):105-116

    Article  Google Scholar 

  46. Nelson A L, Grant E, Barlow G J, Henderson T C (2003) A colony of robots using vision sensing and evolved neural controllers. In: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’03). Las Vegas NV

    Google Scholar 

  47. Gomez F, Miikkulainen R (2004) Transfer of neuroevolved controllers in unstable domains. In: Proceedings of the Genetic and Evolutionary Compu-tation Conference (GECCO-04). Seattle, WA

    Google Scholar 

  48. Earon E J P, Barfoot T D, D’Eleuterio G M T (2000) From the sea to the sidewalk: the evolution of hexapod walking gaits by a genetic algorithm. In: Proceedings of the International Conference on Evolvable Systems (ICES). Edinburgh, Scotland

    Google Scholar 

  49. Zykov V, Bongard J, Lipson H (2004) Evolving dynamic gaits on a physical robot. In: 2004 Genetic and Evolutionary Computation Conference (GECCO). Seattle, WA

    Google Scholar 

  50. Chernova S, Veloso M (2004) An evolutionary approach to gait learning for four-legged robots. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems ( IROS’04), vol. 3. Sendai, Japan

    Google Scholar 

  51. Zufferey J, Floreano D, van Leeuwen M, Merenda T (2002) Evolving vision based flying robot. In: Blthoff, Lee, Poggio, Wallraven (eds) Proceedings of the 2nd International Workshop on Biologically Motivated Computer Vision LNCS 2525. Springer-Verlag, Berlin

    Google Scholar 

  52. Macinnes I, Di Paolo E (2004) Crawling out of the simulation: evolving real robot morphologies using cheap, reusable modules. In: Proceedings of the International Conference on Artificial Life (ALIFE9). MIT Press, Cambridge, MA

    Google Scholar 

  53. Nakamura H, Ishiguro A, Uchilkawa Y (2000) Evolutionary construction of behavior arbitration mechanisms based on dynamically-rearranging neural net-works. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 1

    Google Scholar 

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Nelson, A.L., Grant, E. (2007). Aggregate Selection in Evolutionary Robotics. In: Nedjah, N., Coelho, L.d.S., Mourelle, L.d.M. (eds) Mobile Robots: The Evolutionary Approach. Studies in Computational Intelligence, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49720-2_4

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  • DOI: https://doi.org/10.1007/978-3-540-49720-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

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