Genetic Programming Convergence
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{langdon:GPEM:gpconv,
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author = "W. B. Langdon",
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title = "Genetic Programming Convergence",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2022",
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volume = "23",
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number = "1",
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pages = "71--104",
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month = mar,
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keywords = "genetic algorithms, genetic programming, evolutionary
computation, stochastic search, diversity, bottom up
incremental evaluation, PIE, propagation, infection,
and execution, SIMD parallel processing, AVX vector
instructions",
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ISSN = "1389-2576",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_GPEM_gpconv.pdf",
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URL = "https://rdcu.be/cwoIQ",
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DOI = "doi:10.1007/s10710-021-09405-9",
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video_url = "https://youtu.be/irLoaq6MzbU",
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size = "34 pages",
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abstract = "We study both genotypic and phenotypic convergence in
GP floating point continuous domain symbolic regression
over thousands of generations. Subtree fitness
variation across the population is measured and shown
in many cases to fall. In an expanding region about the
root node, both genetic opcodes and function evaluation
values are identical or nearly identical. Bottom up
(leaf to root) analysis shows both syntactic and
semantic (including entropy) similarity expand from the
outermost node. Despite large regions of zero
variation, fitness continues to evolve and near zero
crossover disruption suggests improved GP systems
within existing memory use.",
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notes = "Two page summary \cite{langdon:2022:GECCOhop_gpem}",
- }
Genetic Programming entries for
William B Langdon
Citations