Genetic programming-based Alife techniques for evolving collective robotic intelligence
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{cho:1999:GPalecri,
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author = "D. Y. Cho and B. T. Zhang",
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title = "Genetic programming-based Alife techniques for
evolving collective robotic intelligence",
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booktitle = "Proceedings 4th International Symposium on Artificial
Life and Robotics",
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year = "1999",
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editor = "M. Sugisaka",
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pages = "236--239",
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address = "B-Con Plaza, Beppu, Oita, Japan",
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month = "19-22 " # jan,
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keywords = "genetic algorithms, genetic programming, artificial
life, multiagent learning, fitness switching, training
data selection",
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URL = "http://bi.snu.ac.kr/Publications/Conferences/International/AROB99.ps",
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URL = "http://citeseer.ist.psu.edu/455064.html",
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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.",
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notes = "AROB'99 Details from www site etc",
- }
Genetic Programming entries for
Dong-Yeon Cho
Byoung-Tak Zhang
Citations