Running programs backwards: instruction inversion for effective search in semantic spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Wieloch:2013:GECCO,
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author = "Bartosz Wieloch and Krzysztof Krawiec",
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title = "Running programs backwards: instruction inversion for
effective search in semantic spaces",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
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isbn13 = "978-1-4503-1963-8",
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pages = "1013--1020",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463493",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "The instructions used for solving typical genetic
programming tasks have strong mathematical properties.
In this study, we leverage one of such properties:
invertibility. A search operator is proposed that
performs an approximate reverse execution of program
fragments, trying to determine in this way the desired
semantics (partial outcome) at intermediate stages of
program execution. The desired semantics determined in
this way guides the choice of a subprogram that
replaces the old program fragment. An extensive
computational experiment on 20 symbolic regression and
Boolean domain problems leads to statistically
significant evidence that the proposed Random Desired
Operator outperforms all typical combinations of
conventional mutation and crossover operators.",
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notes = "Also known as \cite{2463493} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Bartosz Wieloch
Krzysztof Krawiec
Citations