Cartesian Genetic Programming for Control Engineering
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InCollection{Clarke:2017:miller,
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author = "Tim Clarke",
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title = "Cartesian Genetic Programming for Control
Engineering",
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booktitle = "Inspired by Nature: Essays Presented to Julian F.
Miller on the Occasion of his 60th Birthday",
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publisher = "Springer",
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year = "2017",
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editor = "Susan Stepney and Andrew Adamatzky",
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volume = "28",
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series = "Emergence, Complexity and Computation",
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chapter = "7",
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pages = "157--173",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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isbn13 = "978-3-319-67996-9",
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DOI = "doi:10.1007/978-3-319-67997-6_7",
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abstract = "Genetic programming has a proven ability to discover
novel solutions to engineering problems. The author has
worked with Julian F. Miller, together with some
undergraduate and postgraduate students, over the last
ten or so years in exploring innovation through
evolution, using Cartesian Genetic Programming (CGP).
Our co-supervisions and private meetings stimulated
many discussions about its application to a specific
problem domain: control engineering. Initially, we
explored the design of a flight control system for a
single rotor helicopter, where the author has
considerable theoretical and practical experience. The
challenge of taming helicopter dynamics (which are
non-linear, highly cross-coupled and unstable) seemed
ideally suited to the application of CGP. However, our
combined energies drew us towards the more fundamental
issues of how best to generalise the problem with the
objective of freeing up the innovation process from
constrictions imposed by conventional engineering
thinking. This chapter provides an outline of our
thoughts and hopefully may motivate a reader out there
to progress this still embryonic research. The scene is
set by considering a simple class of problems: the
single-input, single-output, linear, time-invariant
system.",
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notes = "part of \cite{miller60book}
https://link.springer.com/bookseries/10624",
- }
Genetic Programming entries for
Tim Clarke
Citations