Dynamic Subset Selection Based on a Fitness Case Topology
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- @Article{lasarczyk:2004:EC,
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author = "Christian W. G. Lasarczyk and Peter Dittrich and
Wolfgang Banzhaf",
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title = "Dynamic Subset Selection Based on a Fitness Case
Topology",
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journal = "Evolutionary Computation",
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year = "2004",
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volume = "12",
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number = "2",
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pages = "223--242",
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month = "Summer",
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keywords = "genetic algorithms, genetic programming, search space,
topology, diversity",
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URL = "http://ls11-www.cs.uni-dortmund.de/people/lasar/publication/LasarDittBanz_TBS_2004/LasarDittBanz_TBS_2004.pdf",
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DOI = "doi:10.1162/106365604773955157",
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size = "20 pages",
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abstract = "A large training set of fitness cases can critically
slow down genetic programming, if no appropriate subset
selection method is applied. Such a method allows an
individual to be evaluated on a smaller subset of
fitness cases. we suggest a subset selection method
that takes the problem structure into account, while
being problem independent at the same time. In order to
achieve this, information about the problem structure
is acquired during evolutionary search by creating a
topology (relationship) on the set of fitness cases.
The topology is induced by individuals of the evolving
population. This is done by increasing the strength of
the relation between two fitness cases, if an
individual of the population is able to solve both of
them. Our new topology based subset selection method
chooses a subset, such that fitness cases in this
subset are as distantly related as is possible with
respect to the induced topology. We compare topology
based selection of fitness cases with dynamic subset
selection and stochastic subset sampling on four
different problems. On average, runs with topology
based selection show faster progress than the others.",
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notes = "preprint at
http://ls11-www.cs.uni-dortmund.de/people/lasar/publication/LasarDittBanz_TBS_2004/LasarDittBanz_TBS_2004.pdf",
- }
Genetic Programming entries for
Christian W G Lasarczyk
Peter Dittrich
Wolfgang Banzhaf
Citations