A CUDA-based Self-adaptive Subpopulation Model in Genetic Programming: cuSASGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Ono:2015:CEC,
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author = "Keiko Ono and Yoshiko Hanada",
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title = "A {CUDA}-based Self-adaptive Subpopulation Model in
Genetic Programming: {cuSASGP}",
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booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary
Computation (CEC 2015)",
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year = "2015",
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editor = "Yadahiko Murata",
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pages = "1543--1550",
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address = "Sendai, Japan",
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month = "25-28 " # may,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, GPU",
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isbn13 = "978-1-4799-7491-7",
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DOI = "doi:10.1109/CEC.2015.7257071",
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abstract = "A parallel model encourages genetic diversity and
frequently shows a better search performance than do
single population models. In the parallel model,
individuals generally migrate to another subpopulation
based on their fitness values, where both the number of
individuals in each subpopulation and the topology are
fixed. To enhance the parallel model in the framework
of genetic programming (GP), it is important to
consider a balance between local and genetic search.
The incorporation of a local search method into the
parallel GP model is a promising approach to enhancing
it. In GP, individuals have various features because of
their structures, and therefore, it is difficult to
determine which feature is the most effective for local
search. Therefore, we propose a novel adaptive
subpopulation model based on various features of
individuals in each generation, in which subpopulations
are adaptively reconstructed based on a fitness value
and the distance between individuals. The proposed
method automatically generates a correlation network on
the basis of the difference between individuals in
terms of not only a fitness value but also node size
and generates subpopulations by network clustering. By
virtue of the reconstruction, individuals with similar
features can evolve in the same subpopulation to
enhance local search. Since, on the one hand, the
generation of a correlation network of individuals
requires considerable computational effort, and on the
other, calculating correlation among individuals is
very suitable for parallelization, we use CUDA to
construct a correlation network. Using three benchmark
problems widely adopted in studies in the literature,
we demonstrate that performance improvement can be
achieved through reconstructing subpopulations based on
a correlation network of individuals, and that the
proposed method significantly outperforms a typical
method.",
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notes = "1005 hrs 15288 CEC2015",
- }
Genetic Programming entries for
Keiko Ono
Yoshiko Hanada
Citations