Rethinking multilevel selection in genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Wu:2011:GECCO,
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author = "Shelly Xiaonan Wu and Wolfgang Banzhaf",
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title = "Rethinking multilevel selection in genetic
programming",
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booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
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year = "2011",
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editor = "Natalio Krasnogor and Pier Luca Lanzi and
Andries Engelbrecht and David Pelta and Carlos Gershenson and
Giovanni Squillero and Alex Freitas and
Marylyn Ritchie and Mike Preuss and Christian Gagne and
Yew Soon Ong and Guenther Raidl and Marcus Gallager and
Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and
Nikolaus Hansen and Silja Meyer-Nieberg and
Jim Smith and Gus Eiben and Ester Bernado-Mansilla and
Will Browne and Lee Spector and Tina Yu and Jeff Clune and
Greg Hornby and Man-Leung Wong and Pierre Collet and
Steve Gustafson and Jean-Paul Watson and
Moshe Sipper and Simon Poulding and Gabriela Ochoa and
Marc Schoenauer and Carsten Witt and Anne Auger",
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isbn13 = "978-1-4503-0557-0",
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pages = "1403--1410",
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keywords = "genetic algorithms, genetic programming",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Dublin, Ireland",
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DOI = "doi:10.1145/2001576.2001765",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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note = "Best paper",
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abstract = "This paper aims to improve the capability of genetic
programming to tackle the evolution of cooperation:
evolving multiple partial solutions that
collaboratively solve structurally and functionally
complex problems. A multilevel genetic programming
approach is presented based on a new computational
multilevel selection framework [19]. This approach
considers biological group selection theory to
encourage cooperation, and a new cooperation operator
to build solutions hierarchically. It extends evolution
from individuals to multiple group levels, leading to
good performance on both individuals and groups. The
applicability of this approach is evaluated on 7
multi-class classification problems with different
features, such as non-linearity, skewed data
distribution and large feature space. The results, when
compared to other cooperative evolutionary algorithms
in the literature, demonstrate that this approach
improves solution accuracy and consistency, and
simplifies solution complexity. In addition, the
problem is decomposed as a result of evolution without
human interference.",
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notes = "Also known as \cite{2001765} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
- }
Genetic Programming entries for
Shelly Xiaonan Wu
Wolfgang Banzhaf
Citations