An investigation of local patterns for estimation of distribution genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Hemberg:2012:GECCO,
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author = "Erik Hemberg and Kalyan Veeramachaneni and
James McDermott and Constantin Berzan and Una-May O'Reilly",
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title = "An investigation of local patterns for estimation of
distribution genetic programming",
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booktitle = "GECCO '12: Proceedings of the fourteenth international
conference on Genetic and evolutionary computation
conference",
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year = "2012",
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editor = "Terry Soule and Anne Auger and Jason Moore and
David Pelta and Christine Solnon and Mike Preuss and
Alan Dorin and Yew-Soon Ong and Christian Blum and
Dario Landa Silva and Frank Neumann and Tina Yu and
Aniko Ekart and Will Browne and Tim Kovacs and
Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and
Giovanni Squillero and Nicolas Bredeche and
Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and
Martin Pelikan and Silja Meyer-Nienberg and
Christian Igel and Greg Hornby and Rene Doursat and
Steve Gustafson and Gustavo Olague and Shin Yoo and
John Clark and Gabriela Ochoa and Gisele Pappa and
Fernando Lobo and Daniel Tauritz and Jurgen Branke and
Kalyanmoy Deb",
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isbn13 = "978-1-4503-1177-9",
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pages = "767--774",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Philadelphia, Pennsylvania, USA",
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DOI = "doi:10.1145/2330163.2330270",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "We present an improved estimation of distribution
(EDA) genetic programming (GP) algorithm which does not
rely upon a prototype tree. Instead of using a
prototype tree, Operator-Free Genetic Programming
learns the distribution of ancestor node chains,
{"}n-grams{"}, in a fit fraction of each generation's
population. It then uses this information, via
sampling, to create trees for the next generation.
Ancestral n-grams are used because an analysis of a GP
run conducted by learning depth first graphical models
for each generation indicated their emergence as
substructures of conditional dependence. We are able to
show that our algorithm, without an operator and a
prototype tree, achieves, on average, performance close
to conventional tree based crossover GP on the problem
we study. Our approach sets a direction for
pattern-based EDA GP which off ers better tractability
and improvements over GP with operators or EDAs using
prototype trees.",
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notes = "Also known as \cite{2330270} GECCO-2012 A joint
meeting of the twenty first international conference on
genetic algorithms (ICGA-2012) and the seventeenth
annual genetic programming conference (GP-2012)",
- }
Genetic Programming entries for
Erik Hemberg
Kalyan Veeramachaneni
James McDermott
Constantin Berzan
Una-May O'Reilly
Citations