International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Cooperative Behavior of Distributed Autonomous Robotic Systems Based on Schema Co-Evolutionary Algorithm

  • Sim, Kwee-Bo (School of Electrical and Electronic Engineering, Chung-Ang University)
  • Published : 2002.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In distributed autonomous robotic systems (DARS), each robot must behave by itself according to its states ad environments, and if necessary, must cooperate with other robots in order to carry out their given tasks. Its most significant merit is that they determine their behavior independently, and cooperate with other robots in order to perform the given tasks. Especially, in DARS, it is essential for each robot to have evolution ability in order to increase the performance of system. In this paper, a schema co-evolutionary algorithm is proposed for the evolution of collective autonomous mobile robots. Each robot exchanges the information, chromosome used in this algorithm, through communication with other robots. Each robot diffuses its chromosome to two or more robots, receives other robot's chromosome and creates new species. Therefore if one robot receives another robot's chromosome, the robot creates new chromosome. We verify the effectiveness of the proposed algorithm by applying it to cooperative search problem.

Keywords

References

  1. John, H. Holland, Adaptation Natural and Artificial Systems, Ann Arbor, University of Michigan Press, 1975
  2. Z. Michalewicz, Genetic Atgorithms+Data Structures Evolution Programs, Third Edition, Springer-Verlag, 1995
  3. Melanie Mitchell, An Introduction to Genetic AIgorithm, A Bradford Book, The MIT Press, 1996
  4. John, R. Koza, Genetic Programming: On the Programming of Computers by means of Natural Selection, A Bradford Book, The MIT Press, 1993
  5. John, R. Koza, Genetic Evolution and Co-Evolution of Computer Programs, Artificial Life II, Addison-Wesley, 1991
  6. Rechenberg, I,Cyberetic Solution Path of an Experimental Problem, Ministry of Aviation, Royal Aircraft Establishment (U.K.), 1965
  7. Hans-Paul Schwefel, Evolution and Optimum Seeking, A Wiley-Interscience Publication, John Wiley & Sons, Inc., 1995
  8. Fogel, L. J., Owens, A. J., and Walsh, M. J., Artiftcial Intelligence Through Simulated Evolution, John Wiley, Chichester, UK, 1966
  9. Seth G. Bullock, 'Co-Evolutionary Design: Implications for Evolutionary Robotics,' COGS Technical report CSRP 384, Univ. of Sussex, 1995
  10. W. Daniel Hillis, 'Co-Evolving parasites Improve Simulated Evolution as an Optimization procedure,' Artificial life II, Vol. X, pp. 313-324, 1991
  11. Jan Paredis, 'Co-Evolutionary Computation,' Artificial life, Vol. 2, No. 4, pp. 353-375, 1995
  12. D. W. Lee and K. B. Sim, 'Structure Optimization and learning of Neural Networks by Co-Evolution,' Proc. of The Third International Symposium on Artificial Life and Robotics, Vol. 2, pp. 462-465, 1998
  13. D. Cliff, G. F. Miller, 'Tracking The Red Queen: Measurements of adaptive progress in co-evolution,' COGS Technical Report CSRP 363, Univ. of Sussex, 1995
  14. T. Kuo and S. Y. Hwang, 'A Genetic Algorithm with Disruptive Selection,' IEEE Trans. On Systems, man and Cybernetics, vol. 26, No. 2, pp. 299-307, 1996 https://doi.org/10.1109/3477.485880