Classification as Clustering: A Pareto Cooperative-Competitive GP Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{McIntyre:2011:EC,
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author = "Andrew R. McIntyre and Malcolm I. Heywood",
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title = "Classification as Clustering: A Pareto
Cooperative-Competitive GP Approach",
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journal = "Evolutionary Computation",
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year = "2011",
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volume = "19",
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number = "1",
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pages = "137--166",
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month = "Spring",
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keywords = "genetic algorithms, genetic programming, MOGA,
Pareto",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/EVCO_a_00016",
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size = "30 pages",
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abstract = "Intuitively population based algorithms such as
Genetic Programming provide a natural environment for
supporting solutions that learn to decompose the
overall task between multiple individuals, or a team.
This work presents a framework for evolving teams
without recourse to pre-specifying the number of
cooperating individuals. To do so, each individual
evolves a mapping to a distribution of outcomes that,
following clustering, establishes the parametrisation
of a (Gaussian) local membership function. This gives
individuals the opportunity to represent subsets of
tasks, where the overall task is that of classification
under the supervised learning domain. Thus, rather than
each team member represent an entire class, individuals
are free to identify unique subsets of the overall
classification task. The framework is supported by
techniques from Evolutionary Multi-objective
Optimisation (EMO) and Pareto competitive coevolution.
EMO establishes the basis for encouraging individuals
to provide accurate yet non-overlaping behaviours;
whereas competitive coevolution provides the mechanism
for scaling to potentially large unbalanced data sets.
Benchmarking is performed against recent examples of
non-linear SVM classifiers over twelve UCI data sets
with between 150 and 200,000 training instances.
Solutions from the proposed Coevolutionary
Multi-objective GP framework appear to provide a good
balance between classification performance and model
complexity, especially as the data set instance count
increases.",
- }
Genetic Programming entries for
Andrew R McIntyre
Malcolm Heywood
Citations