Genetic programming-based Alife techniques for evolving collective robotic intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@InProceedings{cho:1999:GPalecri,

• 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 = "http://bi.snu.ac.kr/Publications/Conferences/International/AROB99.ps",
• URL = "http://citeseer.ist.psu.edu/455064.html",
• 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

Citations