Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InProceedings{banzhaf:1997:gabrrfr,
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author = "Wolfgang Banzhaf and Peter Nordin and Markus Olmer",
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title = "Generating Adaptive Behavior for a Real Robot using
Function Regression within Genetic Programming",
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booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
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editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and
Rick L. Riolo",
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year = "1997",
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month = "13-16 " # jul,
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keywords = "genetic algorithms, genetic programming",
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pages = "35--43",
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address = "Stanford University, CA, USA",
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publisher_address = "San Francisco, CA, USA",
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publisher = "Morgan Kaufmann",
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URL = "http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf",
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size = "11 pages",
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abstract = "We discuss the generation of adaptive behaviour for an
autonomous robot within the framework of a special kind
of function regression used in compiling Genetic
Programming (GP). The control strategy for the robot is
derived, using an evolutionary algorithm, from a
continuous improvement of machine language programs
which are varied and selected against each other. We
give an overview of our recent work on several
fundamental behaviors like obstacle avoidance and
object following adapted from programs that were
originally random sequences of commands. It is argued
that the method is generally applicable where there is
a need for quick adaptation within real-time problem
domains",
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notes = "GP-97",
- }
Genetic Programming entries for
Wolfgang Banzhaf
Peter Nordin
Markus Olmer
Citations