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Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

  • Ivan TanevEmail author
Article

Abstract

In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of the Snakebot.

Keywords

Adaptation Genetic programming Grammar Locomotion Snakebot 

Notes

Acknowledgments

The author thanks Katsunori Shimohara, Thomas Ray and Andrzej Buller for their support of this work. The research was supported in part by the National Institute of Information and Communications Technology of Japan.

References

  1. 1.
    Y. Andrusenko Y., “Russian culture navigator: Miturich-Khlebnikovs: art trade runs in the family,” URL: http://www.vor.ru/culture/cultarch191_eng.html.Google Scholar
  2. 2.
    J. Antonisse, “A grammar-based genetic algorithm,” in Foundations of the Genetic Algorithm Workshop (FOGA), G. J. E. Rawlins (ed.), Morgan Kaufmann, CA, pp. 193–204.Google Scholar
  3. 3.
    P. J. Angeline, “Genetic programming and emergent intelligence,” in Advances in Genetic Programming, K. E. Kinnear Jr. (ed.), MIT Press: Cambridge, MA, 1994, pp. 75–98.Google Scholar
  4. 4.
    W. Banzhaf, P. Nordin, R. Keller, and F. Francone, “Genetic Programming: An Introduction,” Morgan Kaufmann: San Francisco, 1998.Google Scholar
  5. 5.
    J. C. Bongard and H. Lipson, “Automated damage diagnosis and recovery for remote robotics,” in Proceedings of the 2004 International Conference on Robotics and Automation (ICRA 2004), IEEE (ed.), IEEE, New York, 2004, pp. 3545–3550.Google Scholar
  6. 6.
    P. Bosman and E. de Jong, “Learning probabilistic tree grammars for genetic programming,” in Proceedings of the 8th International Conference on Parallel Problem Solving from Nature, X. Yao, E. Burke, et al. (eds.), (PPSN-04), Springer: Berlin, 2004, pp. 192–201.Google Scholar
  7. 7.
    J. W. Burdick, J. Radford, and G. S. Chirikjian, “A 'Sidewinding' locomotion gait for hyper-redundant robots,” in Proceedings of the IEEE Int ernational Conf erence on Robotics and Automation (ICRA 1993), IEEE (ed.), Atlanta, USA, IEEE Computer Society Press: Los Alamitos, CA, 1993, pp. 101–106.Google Scholar
  8. 8.
    L. H. Caporale, Darwin in the Genome: Molecular Strategies in Biological Evolution, McGraw-Hill/Contemporary Books: New York, 2002.Google Scholar
  9. 9.
    G. S. Chirikjian and J. W. Burdick, “The kinematics of hyper-redundant robotic locomotion,” IEEE Trans. Robotics and Automation, vol. 11, pp. 781–793, 1995.CrossRefGoogle Scholar
  10. 10.
    K. Dowling, “Limbless locomotion: learning to crawl,” in Proceedings of the International Conference on Robotics and Automation (ICRA 1999), IEEE (ed.), IEEE, New York, 1999, pp. 3001–3006.Google Scholar
  11. 11.
    D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addision-Wesley, 1989.Google Scholar
  12. 12.
    D. E. Goldberg and K. Deb, “A comparative analysis of selection schemes used in genetic algorithms,” in G. Rawlins (ed.), Foundations of Genetic Algorithms, Morgan Kaufmann, San Mateo, 1991, pp. 69–93.Google Scholar
  13. 13.
    R. Grzeszczuk and D. Terzopoulos, “Automated learning of muscle-actuated locomotion through control abstraction,” in Proceedings of the 22nd Annual Conference on Computer Graphics (SIGGRAPH 1995), in Computer Graphics Proceedings, Annual Conference Series, 1995, pp. 63–70.Google Scholar
  14. 14.
    S. Hirose, A. Morishima, and S. Tukagosi, “Design of practical snake vehicle: Articulated body mobile robot KR-II,” in Proc IEEE 5th Int ernational Conf erence on Advanced Robotics, IEEE (ed.), IEEE: New York, 1991, pp. 833–838.Google Scholar
  15. 15.
    S. Hirose, Biologically Inspired Robots: Snake-Like Locomotors and Manipulators, Oxford University Press, 1993.Google Scholar
  16. 16.
    K. Ito, T. Kamegawa, and F. Matsuno, “Extended QDSEGA for controlling real robots-acquisition of locomotion patterns for snake-like robot,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA 2003), IEEE (ed.), IEEE: New York, 2003, pp. 791–796.Google Scholar
  17. 17.
    K. Ito and F. Matsuno, “A study of reinforcement learning for the robot with many degrees of freedom – acquisition of locomotion patterns for multi legged robot,” in Proceedings of IEEE Int ernational Conf erence on Robotics and Automation (ICRA 2002), IEEE (ed.), IEEE: New York, 2002, pp. 3392–3397.Google Scholar
  18. 18.
    S. Kamio, H. Mitsuhashi, and H. Iba, “Integration of genetic programming and reinforcement learning for real robots,” in Proceedings of the Genetic and Evolutionary Computations Conference (GECCO 2003), Erickdr Cantu-Paz, et al. (eds.), Springer: Berlin, 2003, pp. 470–482.Google Scholar
  19. 19.
    H. Kimura, T. Yamashita, and S. Kobayashi, “Reinforcement learning of walking behavior for a four-legged robot,” in Proceedings of the 40th IEEE Conference on Decision and Control (CDC 2001), J. Jim Zhu (ed.), IEEE: New York, 2001, pp. 411–416.Google Scholar
  20. 20.
    M. W. Kirschner and J. C. Gerhart, The Plausibility of Life: Resolving Darwin's Dilemma, Yale University Press, 2005.Google Scholar
  21. 21.
    J. R. Koza, Genetic Programming: on the Programming of Computers by Means of Natural Selection, MIT Press: Cambridge, MA, 1992.zbMATHGoogle Scholar
  22. 22.
    J. R. Koza, M. A. Keane, J. Yu, F. H. Bennett III, and W. Mydlowec, “Automatic creation of human-competitive programs and controllers by means of genetic programming,” Genetic Programming and Evolvable Machines, vol. 1, pp. 121–164, 2000.zbMATHCrossRefGoogle Scholar
  23. 23.
    I. B. Levitan and L. K. Kaczmarek, The Neuron: Cell and Molecular Biology, Oxford University Press: New York, 2002.Google Scholar
  24. 24.
    S. Mahdavi and P. J. Bentley, “Evolving motion of robots with muscles,” in Proc of the the EvoROB2003, the 2nd European Workshop on Evolutionary Robotics, C. Ryan, T. Soule, et al. (eds.), EuroGP 2003, Springer: Berlin, pp. 655–664.Google Scholar
  25. 25.
    M. O'Neill and C. Ryan, Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language, Springer: Berlin, 2003.zbMATHGoogle Scholar
  26. 26.
    J. Ostrowski and J. Burdick, “Gait kinematics for a serpentine robot,” in Proceedings of the IEEE Int ernational Conf erence on Rob otics and Autom ation (ICRA 1996), IEEE (ed.), IEEE: New York, 1996, pp. 1294–1299.Google Scholar
  27. 27.
    M. Pelikan, D. E. Goldberg, and E.Cantú-Paz, “BOA: the Bayesian optimization algorithm,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), W. Banzhaf, J. Daida et al. (eds.), Morgan Kaufmann Publishers: San Francisco, CA, 1999, pp. 525–532.Google Scholar
  28. 28.
    R. Pfeifer and C.Scheier, Understanding Intelligence, MIT Press: Cambridge, 2001.Google Scholar
  29. 29.
    B. Salemi, P. Will, and W.-M. Shen, “Distributed task negotiation in self-reconfigurable robots,” in Proceedings of the IEEE/ RSJ International Conference on Intelligent Robotics and systems (IROS 2003), IEEE (ed.), IEEE: New York, 2003, pp. 2448–2453.Google Scholar
  30. 30.
    R. Salustowicz and J. Schmidhuber, “Probabilistic incremental program evolution,” Evolutionary Computation, vol. 5, pp. 123–141, 1997.Google Scholar
  31. 31.
    Y. Shan, R. I. McKay, and R. Baxter, “Grammar model-based program evolution,” in Proceedings of the 2004 IEEE Congress on Evolutionary Computation, IEEE (ed.), Portland, Oregon, IEEE, New York, 20–23 June, 2004, pp. 478–485.Google Scholar
  32. 32.
    R. Smith, “Open Dynamics Engine,” 2001–2006, URL: http://q12.org/ode/Google Scholar
  33. 33.
    K. Stoy, W.-M. Shen, and P. M. Will, “A simple approach to the control of locomotion in self-reconfigurable robots,” Robotics and Autonomous Systems, vol. 44, pp. 191–200, 2003.CrossRefGoogle Scholar
  34. 34.
    G. S. Hornby, S. Takamura, T. Yamamoto, and M. Fujita, “Autonomous evolution of dynamic gaits with two quadruped robots,” IEEE Transactions on Robotics, vol. 21, pp. 402–410, 2005.CrossRefGoogle Scholar
  35. 35.
    I. Tanev and T. Ray, “Evolution of sidewinding locomotion of simulated limbless, wheelless robots,” Artificial Life and Robotics, Springer, 2005, vol. 9, pp. 117–122.Google Scholar
  36. 36.
    I. Tanev, T. Ray, and A. Buller, “Automated evolutionary design, robustness and adaptation of sidewinding locomotion of simulated Snake-like robot,” IEEE Transactions on Robotics, vol. 21, pp. 632–645, 2005.CrossRefGoogle Scholar
  37. 37.
    M. L. Wong, “Evolving recursive programs by using adaptive grammar based genetic programming,” Genetic Programming and Evolvable Machines, vol. 6, pp. 421–455, 2005.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Information Systems DesignFaculty of Engineering, Doshisha UniversityKyotanabeJapan

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