Strength Through Diversity: Disaggregation and Multi-Objectivisation Approaches for Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Fieldsend:2015:GECCO,
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author = "Jonathan E. Fieldsend and Alberto Moraglio",
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title = "Strength Through Diversity: Disaggregation and
Multi-Objectivisation Approaches for Genetic
Programming",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "1031--1038",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, optimisation,
multi-objectivisation, diversity",
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URL = "http://doi.acm.org/10.1145/2739480.2754643",
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DOI = "doi:10.1145/2739480.2754643",
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URL = "https://core.ac.uk/download/pdf/83924170.pdf",
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URL = "https://github.com/fieldsend/gecco_2015_mogp",
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size = "8 pages",
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abstract = "An underlying problem in genetic programming (GP) is
how to ensure sufficient useful diversity in the
population during search. Having a wide range of
diverse (sub)component structures available for
recombination and/or mutation is important in
preventing premature converge. We propose two new
fitness disaggregation approaches that make explicit
use of the information in the test cases (i.e., program
semantics) to preserve diversity in the population. The
first method preserves the best programs which pass
each individual test case, the second preserves those
which are non-dominated across test cases
(multi-objectivisation). We use these in standard GP,
and compare them to using standard fitness sharing, and
using standard (aggregate) fitness in tournament
selection. We also examine the effect of including a
simple anti-bloat criterion in the selection mechanism.
We find that the non-domination approach, employing
anti-bloat, significantly speeds up convergence to the
optimum on a range of standard Boolean test problems.
Furthermore, its best performance occurs with a
considerably smaller population size than typically
employed in GP.",
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notes = "Also known as \cite{2754643} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Jonathan E Fieldsend
Alberto Moraglio
Citations