A schema theory analysis of mutation size biases in genetic programming with linear representations
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @TechReport{McPhee00-24,
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author = "Nicholas Freitag McPhee and Riccardo Poli and
Jon E Rowe",
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title = "A schema theory analysis of mutation size biases in
genetic programming with linear representations",
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institution = "University of Birmingham, School of Computer Science",
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number = "CSRP-00-24",
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month = nov,
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year = "2000",
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email = "N.F.McPhee@cs.bham.ac.uk, R.Poli@cs.bham.ac.uk
N.F.McPhee@cs.bham.ac.uk",
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keywords = "genetic algorithms, genetic programming",
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file = "/2000/CSRP-00-24.ps.gz",
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URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2000/CSRP-00-24.ps.gz",
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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