Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules)
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- @InProceedings{Saini:2021:GPTP,
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author = "Anil Kumar Saini and Lee Spector",
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title = "Evolving and Analyzing Modularity with {GLEAM}
(Genetic Learning by Extraction and Absorption of
Modules)",
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booktitle = "Genetic Programming Theory and Practice XVIII",
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year = "2021",
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editor = "Wolfgang Banzhaf and Leonardo Trujillo and
Stephan Winkler and Bill Worzel",
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series = "Genetic and Evolutionary Computation",
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pages = "181--195",
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address = "East Lansing, USA",
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month = "19-21 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-16-8112-7",
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DOI = "doi:10.1007/978-981-16-8113-4_10",
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abstract = "General methods for the evolution of programs with
modular structure have long been sought by genetic
programming researchers, in part because modularity has
long been considered to be essential, or at least
helpful, for human programmers when they develop
large-scale software projects. Multiple efforts have
been made in this direction, and while success has been
demonstrated in specific contexts, no general scheme
has yet been demonstrated to provide benefits for
evolutionary program synthesis that are similar in
generality and significance to those provided by
modularity in human software engineering. we present
and analyse a new framework for the study of the
evolution of modularity, called GLEAM (Genetic Learning
by Extraction and Absorption of Modules). GLEAMs
flexible architecture and tunable parameters allow
researchers to test different methods related to the
generation, propagation, and use of modules in genetic
programming.",
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notes = "Part of \cite{Banzhaf:2021:GPTP} published after the
workshop in 2022",
- }
Genetic Programming entries for
Anil Kumar Saini
Lee Spector
Citations