Improvement of Code Fragment Fitness to Guide Feature Construction in XCS
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Nguyen:2019:GECCOa,
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author = "Trung B. Nguyen and Will N. Browne and Mengjie Zhang",
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title = "Improvement of Code Fragment Fitness to Guide Feature
Construction in {XCS}",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2019",
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editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and
Anne Auger and Petr Posik and Leslie {Peprez Caceres} and
Andrew M. Sutton and Nadarajen Veerapen and
Christine Solnon and Andries Engelbrecht and Stephane Doncieux and
Sebastian Risi and Penousal Machado and
Vanessa Volz and Christian Blum and Francisco Chicano and
Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and
Jonathan Fieldsend and Jose Antonio Lozano and
Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and
Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and
Robin Purshouse and Thomas Baeck and Justyna Petke and
Giuliano Antoniol and Johannes Lengler and
Per Kristian Lehre",
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isbn13 = "978-1-4503-6111-8",
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pages = "428--436",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3321707.3321751",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, XCS, XCSCFC,
LCS, code fragment",
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size = "9 pages",
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abstract = "In complex classification problems, constructed
features with rich discriminative information can
simplify decision boundaries. Code Fragments (CFs)
produce GP-tree-like constructed features that can
represent decision boundaries effectively in Learning
Classifier Systems (LCSs). But the search space for
useful CFs is vast due to this richness in boundary
creation, which is impractical. Online
Feature-generation (OF) improves the search of useful
CFs by growing promising CFs from a dynamic list of
preferable CFs based on the ability to produce accurate
and generalised, i.e. high-fitness, classifiers.
However, the previous preference for high-numerosity
CFs did not encapsulate information about the
applicability of CFs directly. Consequently, learning
performances of OF with an accuracy-based LCS (termed
XOF) struggled to progress in the final learning phase.
The hypothesis is that estimating the CF-fitness of CFs
based on classifier fitness will aid the search for
useful constructed features. This is anticipated to
drive the search of decision boundaries efficiently,
and thereby improve learning performances. Experiments
on large-scale and hierarchical Boolean problems show
that the proposed systems learn faster than traditional
LCSs regarding the number of generations and time
consumption. Tests on real-world datasets demonstrate
its capability to find readable and useful features to
solve practical problems.",
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notes = "Also known as \cite{3321751} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Trung Bao Nguyen
Will N Browne
Mengjie Zhang
Citations