Efficient tree traversal to reduce code growth in tree-based genetic programming

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

@Article{Wyns:2009:JH,

• title = "Efficient tree traversal to reduce code growth in tree-based genetic programming",
• author = "Bart Wyns and Luc Boullart",
• year = "2009",
• journal = "Journal of Heuristics",
• volume = "15",
• number = "1",
• pages = "77--104",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Subtree fitness, Tree traversal, Code growth, Local optimization, Tree-based genetic programming, Technology and Engineering",
• ISSN = "1381-1231",
• DOI = "doi:10.1007/s10732-007-9060-0",
• bibsource = "OAI-PMH server at biblio.ugent.be",
• oai = "oai:archive.ugent.be:662689",
• abstract = "Genetic programming is an evolutionary optimization method following the principle of program induction. Genetic programming often uses variable-length tree structures for representing candidate solutions. A serious problem with variable-length representations is code growth: during evolution these tree structures tend to grow in size without a corresponding increase in fitness. Many anti-bloat methods focus solely on size reduction and forget about fitness improvement, which is rather strange when using an {"}optimization{"} method. This paper reports the application of a semantically driven local search operator to control code growth and improve best fitness. Five examples, two theoretical benchmark applications and three real-life test problems are used to illustrate the obtained size reduction and fitness improvement. Performance of the local search operator is also compared with various other anti-bloat methods such as size and depth delimiters, an expression simplifier, linear and adaptive parsimony pressure, automatically defined functions and Tarpeian bloat control.",

}

Genetic Programming entries for Bart Wyns Luc Boullart

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