An efficient distance metric for linear genetic programming
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- @InProceedings{Gaudesi:2013:GECCO,
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author = "Marco Gaudesi and Giovanni Squillero and
Alberto Tonda",
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title = "An efficient distance metric for linear genetic
programming",
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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 = "925--932",
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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:10.1145/2463372.2463495",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Defining a distance measure over the individuals in
the population of an Evolutionary Algorithm can be
exploited for several applications, ranging from
diversity preservation to balancing exploration and
exploitation. When individuals are encoded as strings
of bits or sets of real values, computing the distance
between any two can be a straightforward process; when
individuals are represented as trees or linear graphs,
however, quite often the user must resort to
phenotype-level problem-specific distance metrics. This
paper presents a generic genotype-level distance metric
for Linear Genetic Programming: the information
contained by an individual is represented as a set of
symbols, using n-grams to capture significant recurring
structures inside the genome. The difference in
information between two individuals is evaluated
resorting to a symmetric difference. Experimental
evaluations show that the proposed metric has a strong
correlation with phenotype-level problem-specific
distance measures in two problems where individuals
represent string of bits and Assembly-language
programs, respectively.",
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notes = "Also known as \cite{2463495} 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
Marco Gaudesi
Giovanni Squillero
Alberto Tonda
Citations