Search Operator Bias in Linearly Structured Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{wilson:2004:lbp,

• author = "Garnett C. Wilson and Malcolm I. Heywood",
• title = "Search Operator Bias in Linearly Structured Genetic Programming",
• booktitle = "Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference",
• year = "2004",
• editor = "Maarten Keijzer",
• address = "Seattle, Washington, USA",
• month = "26 " # jul,
• keywords = "genetic algorithms, genetic programming",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2004/LBP003.pdf",
• abstract = "GA solutions to the job-shop scheduling problem demonstrate that significant amounts of code context exist. Such observations have led to the introduction of biased search operators. In this work, we recognise that similar conditions exist in linearly structured GP (L-GP). An empirical study is made when biased search operators are applied to the San Mateo Trail (strategy), Two Box (regression), and Liver Disease (classification) benchmark problems. A preference is observed for biased mutation alone in the case of the regression problem, whereas the strategy and classification problems appear to prefer the combination of both biased mutation and crossover.",
• notes = "Part of \cite{keijzer:2004:GECCO:lbp}",

}

Genetic Programming entries for Garnett Carl Wilson Malcolm Heywood

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