A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ragalo:2012:NaBIC,

• author = "Anisa W. Ragalo and Nelishia Pillay",
• booktitle = "Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC 2012)",
• title = "A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming",
• year = "2012",
• pages = "123--129",
• address = "Mexico City",
• month = "5-9 " # nov,
• publisher = "IEEE",
• DOI = "doi:10.1109/NaBIC.2012.6402250",
• size = "7 pages",
• abstract = "Polynomial Symbolic Regression tree-based Genetic Programming faces considerable obstacles towards the discovery of a global optimum solution; three of these being bloat, premature convergence and a compromised ability to retain building block information. We present a building block conservation and extension strategy that targets these specific obstacles. Experiments conducted demonstrate a superior performance of our strategy relative to the canonical GP. Further our strategy achieves a competitive reduction in bloat.",
• keywords = "genetic algorithms, genetic programming, convergence, regression analysis, trees (mathematics), building block conservation, canonical GP, extension mechanism, global optimum solution, polynomial symbolic regression tree-based genetic programming, premature convergence, Convergence, Genetics, Materials, Polynomials, Regression tree analysis, Sociology, Statistics, Dynamic Maximum Depth, Local Optima, Premature Convergence, Symbolic Regression",
• notes = "Also known as \cite{6402250}",

}

Genetic Programming entries for Anisa Waganda Ragalo Nelishia Pillay

Citations