A schema theory analysis of mutation size biases in genetic programming with linear representations

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

@TechReport{McPhee00-24,

• author = "Nicholas Freitag McPhee and Riccardo Poli and Jon E Rowe",
• title = "A schema theory analysis of mutation size biases in genetic programming with linear representations",
• institution = "University of Birmingham, School of Computer Science",
• number = "CSRP-00-24",
• month = nov,
• year = "2000",
• email = "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk N.F.McPhee@cs.bham.ac.uk",
• keywords = "genetic algorithms, genetic programming",
• file = "/2000/CSRP-00-24.ps.gz",
• URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-24.ps.gz",
• abstract = "In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length linear structures. In this paper we use the schema theory to better understand the biases induced on linear structures by two common GP subtree mutation operators: FULL and GROW mutation. In both cases we find that the operators do have quite specific biases and typically strongly oversample shorter strings.",

}

Genetic Programming entries for Nicholas Freitag McPhee Riccardo Poli Jonathan E Rowe

Citations