The Rubik Cube and GP Temporal Sequence Learning: An Initial Study
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Lichodzijewski:2010:GPTP,
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author = "Peter Lichodzijewski and Malcolm Heywood",
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title = "The {Rubik Cube} and {GP} Temporal Sequence Learning:
An Initial Study",
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booktitle = "Genetic Programming Theory and Practice VIII",
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year = "2010",
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editor = "Rick Riolo and Trent McConaghy and
Ekaterina Vladislavleva",
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series = "Genetic and Evolutionary Computation",
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volume = "8",
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address = "Ann Arbor, USA",
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month = "20-22 " # may,
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publisher = "Springer",
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chapter = "3",
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pages = "35--54",
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keywords = "genetic algorithms, genetic programming, bid-based
cooperative behaviours, problem decomposition, Rubik
cube, symbiotic coevolution, temporal sequence
learning",
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isbn13 = "978-1-4419-7746-5",
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URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
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DOI = "doi:10.1007/978-1-4419-7747-2_3",
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abstract = "The 3 by 3 Rubik cube represents a potential benchmark
for temporal sequence learning under a discrete
application domain with multiple actions. Challenging
aspects of the problem domain include the large state
space and a requirement to learn invariances relative
to the specific colours present the latter element of
the domain making it difficult to evolve individuals
that learn macro-moves relative tomultiple cube
configurations. An initial study is presented in
thiswork to investigate the utility ofGenetic
Programming capable of layered learning and problem
decomposition. The resulting solutions are tested on
5000 test cubes, of which specific individuals are able
to solve up to 350 (7 percent) cube configurations and
population wide behaviours are capable of solving up to
1200 (24 percent) of the test cube configurations. It
is noted that the design options for generic fitness
functions are such that users are likely to face either
reward functions that are very expensive to evaluate or
functions that are very deceptive. Addressing this
might well imply that domain knowledge is explicitly
used to decompose the task to avoid these challenges.
This would augment the described generic approach
currently employed for Layered learning, problem
decomposition.",
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notes = "part of \cite{Riolo:2010:GPTP}",
- }
Genetic Programming entries for
Peter Lichodzijewski
Malcolm Heywood
Citations