Long-Term Evolution Experiment with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Langdon:2022:ALJ,
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author = "William B. Langdon and Wolfgang Banzhaf",
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title = "Long-Term Evolution Experiment with Genetic
Programming",
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journal = "Artificial Life",
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year = "2022",
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volume = "28",
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number = "2",
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pages = "173--204",
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month = "Summer",
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note = "Invited submission to Artificial Life Journal special
issue of the ALIFE'19 conference",
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keywords = "genetic algorithms, genetic programming, GA, GP,
convergence, extended unlimited evolution, information
theory limit on complexity",
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ISSN = "1064-5462",
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publisher = "MIT Press",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2022_ALJ.pdf",
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DOI = "doi:10.1162/artl_a_00360",
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video_url = "https://youtu.be/xfJtsWiM7Oo",
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video_url = "https://youtu.be/TssAIo-vatE",
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code_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/GPavx.tar.gz",
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size = "40 pages",
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abstract = "We evolve floating point Sextic polynomial populations
of genetic programming binary trees for up to a million
generations. We observe continued innovation but this
is limited by their depth and suggest deep expressions
are resilient to learning as they disperse information,
impeding evolvability and the adaptation of highly
nested organisms and instead we argue for open
complexity. Programs with more than 2000000000
instructions (depth 20000) are created by crossover. To
support unbounded long-term evolution experiments LTEE
in GP we use incremental fitness evaluation and both
SIMD parallel AVX 512 bit instructions and 16 threads
to yield performance equivalent of up to 1.1 trillion
GP operations per second, 1.1 tera GPops, on an Intel
Xeon Gold 6136 CPU 3.00GHz server.",
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notes = "Journal extension of \cite{Langdon:2019:alife}
Two page summary in
\cite{langdon:2022:GECCOhop_alj}
Code
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/GPavx.tar.gz",
- }
Genetic Programming entries for
William B Langdon
Wolfgang Banzhaf
Citations