Coevolutionary bid-based genetic programming for problem decomposition in classification
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gp-bibliography.bib Revision:1.8081
- @Article{Lichodzijewski:2008:GPEM,
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author = "Peter Lichodzijewski and Malcolm I. Heywood",
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title = "Coevolutionary bid-based genetic programming for
problem decomposition in classification",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2008",
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volume = "9",
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number = "4",
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pages = "331--365",
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month = dec,
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keywords = "genetic algorithms, genetic programming, Coevolution,
Problem decomposition, Teaming, Classification, SVM",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-008-9067-9",
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abstract = "In this work a cooperative, bid-based, model for
problem decomposition is proposed with application to
discrete action domains such as classification. This
represents a significant departure from models where
each individual constructs a direct input-outcome map,
for example, from the set of exemplars to the set of
class labels as is typical under the classification
domain. In contrast, the proposed model focuses on
learning a bidding strategy based on the exemplar
feature vectors; each individual is associated with a
single discrete action and the individual with the
maximum bid wins the right to suggest its action. Thus,
the number of individuals associated with each action
is a function of the intra-action bidding behaviour.
Credit assignment is designed to reward correct but
unique bidding strategies relative to the target
actions. An advantage of the model over other teaming
methods is its ability to automatically determine the
number of and interaction between cooperative team
members. The resulting model shares several traits with
learning classifier systems and as such both approaches
are benchmarked on nine large classification problems.
Moreover, both of the evolutionary models are compared
against the deterministic Support Vector Machine
classification algorithm. Performance assessment
considers the computational, classification, and
complexity characteristics of the resulting solutions.
The bid-based model is found to provide simple yet
effective solutions that are robust to wide variations
in the class representation. Support Vector Machines
and classifier systems tend to perform better under
balanced datasets albeit resulting in black-box
solutions.",
- }
Genetic Programming entries for
Peter Lichodzijewski
Malcolm Heywood
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