A comparison of grammatical genetic programming grammars for controlling femtocell network coverage
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Hemberg:2013:GPEM,
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author = "Erik Hemberg and Lester Ho and Michael O'Neill and
Holger Claussen",
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title = "A comparison of grammatical genetic programming
grammars for controlling femtocell network coverage",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2013",
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volume = "14",
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number = "1",
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pages = "65--93",
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month = mar,
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Grammars, Femtocell, Symbolic regression",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-012-9171-8",
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size = "20 pages",
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abstract = "We study grammars used in grammatical genetic
programming (GP) which create algorithms that control
the base station pilot power in a femtocell network.
The overall goal of evolving algorithms for femtocells
is to create a continuous online evolution of the
femtocell pilot power control algorithm in order to
optimise their coverage. We compare the performance of
different grammars and analyse the femtocell simulation
model using the grammatical genetic programming method
called grammatical evolution. The grammars consist of
conditional statements or mathematical functions as are
used in symbolic regression applications of GP, as well
as a hybrid containing both kinds of statements. To
benchmark and gain further information about our
femtocell network simulation model we also perform
random sampling and limited enumeration of femtocell
pilot power settings. The symbolic regression based
grammars require the most configuration of the
evolutionary algorithm and more fitness evaluations,
whereas the conditional statement grammar requires more
domain knowledge to set the parameters. The content of
the resulting femtocell algorithms shows that the
evolutionary computation (EC) methods are exploiting
the assumptions in the model. The ability of EC to
exploit bias in both the fitness function and the
underlying model is vital for identifying the current
system and improves the model and the EC method.
Finally, the results show that the best fitness and
engineering performances for the grammars are similar
over both test and training scenarios. In addition, the
evolved solutions' performance is superior to those
designed by humans.",
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notes = "Recommended by Una-May O'Reilly and Steven
Gustafson.",
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affiliation = "Complex and Adaptive Systems Laboratory, School of
Computer Science and Informatics, University College
Dublin, Dublin, Ireland",
- }
Genetic Programming entries for
Erik Hemberg
Lester T W Ho
Michael O'Neill
Holger Claussen
Citations