Numeric Mutation as an Improvement to Symbolic Regression in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{fernandez:1998:nmisrGP,

• author = "Thomas Fernandez and Matthew Evett",
• title = "Numeric Mutation as an Improvement to Symbolic Regression in Genetic Programming",
• booktitle = "Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming",
• year = "1998",
• editor = "V. William Porto and N. Saravanan and D. Waagen and A. E. Eiben",
• volume = "1447",
• series = "LNCS",
• pages = "251--260",
• address = "Mission Valley Marriott, San Diego, California, USA",
• publisher_address = "Berlin",
• month = "25-27 " # mar,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-64891-7",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fernandez_1998_nmisrGP.pdf",
• URL = "https://rdcu.be/daynn",
• DOI = "doi:10.1007/BFb0040778",
• size = "10 pages",
• abstract = "A weakness of genetic programming (GP) is the difficulty it suffers in discovering useful numeric constants for the terminal nodes of the s-expression trees. We examine a solution to this problem called numeric mutation based roughly on simulated annealing. We provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system performance for symbolic regression problems. GP runs are more likely to find a solution and successful runs use fewer generations",
• notes = "EP-98. Florida Atlantic University, Boca Raton, FL",

}

Genetic Programming entries for Thomas Fernandez Matthew P Evett

Citations