Problem Decomposition Strategies and Credit Distribution Mechanisms in Modular Genetic Programming for Supervised Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8344
- @Article{Rodriguez-Coayahuitl:TEVC,
-
author = "Lino Rodriguez-Coayahuitl and
Ansel Y. Rodriguez-Gonzalez and Daniel Fajardo-Delgado and
Maria Guadalupe {Sanchez Cervantes}",
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title = "Problem Decomposition Strategies and Credit
Distribution Mechanisms in Modular Genetic Programming
for Supervised Learning",
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journal = "IEEE Transactions on Evolutionary Computation",
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keywords = "genetic algorithms, genetic programming, Reviews,
Evolutionary computation, Supervised learning,
Taxonomy, Estimation, Deep learning, Syntactics, Random
forests, problem decomposition, Machine Learning,
Cooperative Coevolution, modularity, ADF",
-
ISSN = "1941-0026",
-
DOI = "
doi:10.1109/TEVC.2025.3526581",
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abstract = "In this review article, we provide a comprehensive
guide to the endeavor of problem decomposition within
the field of Genetic Programming (GP), specifically
tree-based GP for supervised learning tasks. We
analysed in detail 70 manuscripts that deal with motifs
such as 'problem decomposition'', modular GP'',
subroutine evolution'', hierarchical GP'', cooperative
coevolution', among others. As a result of this study,
we propose an unifying taxonomy that categorizes
efforts on problem decomposition in GP along three
major axes: the architecture of evolved composite
solutions, problem decomposition strategy, and credit
assignment approach. This classification system sheds
light on how the diverse proposed methodologies for
problem decomposition relate to each other and where
most of the research efforts have focused to this day.
Rather than discussing in detail any particular set of
works, we see this overview as a map that may help
researchers in obtaining a wider view of existing
efforts for problem decomposition in GP, as well as
provide a cohesive framework that allows the disclosure
of future developments in clearly differentiated
niches. We close the article with a brief analysis that
compares the current state of problem decomposition
methodologies in GP with that of another exemplar of
problem decomposition in machine learning: deep
learning.",
-
notes = "Also known as \cite{10829831}",
- }
Genetic Programming entries for
Lino Rodriguez-Coayahuitl
Ansel Y Rodriguez-Gonzalez
Daniel Fajardo-Delgado
Maria Guadalupe Sanchez-Cervantes
Citations