On the Nature of the Phenotype in Tree Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{banzhaf:2024:GECCO,
-
author = "Wolfgang Banzhaf and Illya Bakurov",
-
title = "On the Nature of the Phenotype in Tree Genetic
Programming",
-
booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
-
year = "2024",
-
editor = "Ting Hu and Aniko Ekart and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and Ying Bi and
Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and
Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and
Frank Neumann and Carla Silva",
-
pages = "868--877",
-
address = "Melbourne, Australia",
-
series = "GECCO '24",
-
month = "14-18 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming,
genotype-phenotype map, simplication, neutrality,
explainability, symbolic regression",
-
isbn13 = "979-8-4007-0494-9",
-
DOI = "doi:10.1145/3638529.3654129",
-
size = "10 pages",
-
abstract = "In this contribution, we discuss the basic concepts of
genotypes and phenotypes in tree-based GP (TGP), and
then analyze their behavior using five real-world
datasets. We show that TGP exhibits the same behavior
that we can observe in other GP representations: At the
genotypic level trees show frequently unchecked growth
with seemingly ineffective code, but on the phenotypic
level, much smaller trees can be observed. To generate
phenotypes, we provide a unique technique for removing
semantically ineffective code from GP trees. The
approach extracts considerably simpler phenotypes while
not being limited to local operations in the genotype.
We generalize this transformation based on a
problem-independent parameter that enables a further
simplification of the exact phenotype by
coarse-graining to produce approximate phenotypes. The
concept of these phenotypes (exact and approximate)
allows us to clarify what evolved solutions truly
predict, making GP models considered at the phenotypic
level much better interpretable.",
-
notes = "GECCO-2024 GP A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Wolfgang Banzhaf
Illya Bakurov
Citations