Genetic programming-based Alife techniques for evolving collective robotic intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "D. Y. Cho and B. T. Zhang",
  title =        "Genetic programming-based Alife techniques for
                 evolving collective robotic intelligence",
  booktitle =    "Proceedings 4th International Symposium on Artificial
                 Life and Robotics",
  year =         "1999",
  editor =       "M. Sugisaka",
  pages =        "236--239",
  address =      "B-Con Plaza, Beppu, Oita, Japan",
  month =        "19-22 " # jan,
  keywords =     "genetic algorithms, genetic programming, artificial
                 life, multiagent learning, fitness switching, training
                 data selection",
  URL =          "",
  URL =          "",
  abstract =     "Control strategies for a multiple robot system should
                 be adaptive and decentralized like those of social
                 insects. To evolve this kind of control programs, we
                 use genetic programming (GP). However, conventional GP
                 methods are difficult to evolve complex coordinated
                 behaviors and not powerful enough to solve the class of
                 problems which require some emergent behaviors to be
                 achieved in sequence. In a previous work, we presented
                 a novel method called fitness switching. Here we extend
                 the fitness switching method by introducing the concept
                 of active data selection to further accelerate
                 evolution speed of GP. Experimental results are
                 reported on a table transport problem in which multiple
                 autonomous mobile robots should cooperate to transport
                 a large and heavy table.",
  notes =        "AROB'99 Details from www site etc",

Genetic Programming entries for Dong-Yeon Cho Byoung-Tak Zhang