Fitness Distance Correlation in Structural Mutation Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{vanneschi03,

• author = "Leonardo Vanneschi and Marco Tomassini and Philippe Collard and Manuel Clergue",
• title = "Fitness Distance Correlation in Structural Mutation Genetic Programming",
• booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
• year = "2003",
• editor = "Conor Ryan and Terence Soule and Maarten Keijzer and Edward Tsang and Riccardo Poli and Ernesto Costa",
• volume = "2610",
• series = "LNCS",
• pages = "455--464",
• address = "Essex",
• publisher_address = "Berlin",
• month = "14-16 " # apr,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming: Poster",
• ISBN = "3-540-00971-X",
• DOI = "doi:10.1007/3-540-36599-0_43",
• abstract = "A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.",
• notes = "EuroGP'2003 held in conjunction with EvoWorkshops 2003",

}

Genetic Programming entries for Leonardo Vanneschi Marco Tomassini Philippe Collard Manuel Clergue

Citations