Evolving and Analyzing Modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules)

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Saini:2021:GPTP,

• author = "Anil Kumar Saini and Lee Spector",
• title = "Evolving and Analyzing Modularity with {GLEAM} (Genetic Learning by Extraction and Absorption of Modules)",
• booktitle = "Genetic Programming Theory and Practice XVIII",
• year = "2021",
• editor = "Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel",
• series = "Genetic and Evolutionary Computation",
• pages = "181--195",
• address = "East Lansing, USA",
• month = "19-21 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-981-16-8112-7",
• DOI = "doi:10.1007/978-981-16-8113-4_10",
• 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.",
• notes = "Part of \cite{Banzhaf:2021:GPTP} published after the workshop in 2022",

}

Genetic Programming entries for Anil Kumar Saini Lee Spector

Citations