Runtime phylogenetic analysis enables extreme subsampling for test-based problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{lalejini:2024:GECCOcomp,
-
author = "Alexander Lalejini and Marcos Sanson and
Jack Garbus and Matthew Andres Moreno and Emily Dolson",
-
title = "Runtime phylogenetic analysis enables extreme
subsampling for test-based problems",
-
booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
-
year = "2024",
-
editor = "Ting Hu and Aniko Ekart",
-
pages = "511--514",
-
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, subsampling,
selection schemes, lexicase selection, phylogenetic
analysis, test-based problems: Poster",
-
isbn13 = "979-8-4007-0495-6",
-
DOI = "doi:10.1145/3638530.3654208",
-
size = "4 pages",
-
abstract = "A phylogeny describes a population's evolutionary
history. Evolutionary search algorithms can perfectly
track the ancestry of candidate solutions, illuminating
a population's trajectory through the search space. We
introduce phylogeny-informed subsampling, a new class
of subsampling methods that exploit runtime
phylogenetic analyses for solving test-based problems.
Specifically, we assess two phylogeny-informed
subsampling methods---individualized random subsampling
and ancestor-based subsampling---on ten genetic
programming (GP) problems from program synthesis
benchmark suites. Overall, we find that
phylogeny-informed subsampling methods enable
problem-solving success at extreme subsampling levels
where other subsampling methods fail. For example,
phylogeny-informed subsampling methods more reliably
solved program synthesis problems when evaluating just
one training case per-individual, per-generation.
However, at moderate subsampling levels,
phylogeny-informed subsampling generally performed no
better than random subsampling on GP problems.
Continued refinements of phylogeny-informed subsampling
techniques offer a promising new direction for scaling
up evolutionary systems to handle problems with many
expensive-to-evaluate fitness criteria.",
-
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
Alexander Lalejini
Marcos Sanson
Jack Garbus
Matthew Andres Moreno
Emily Dolson
Citations