On the robustness of lexicase selection to contradictory objectives
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{shahbandegan:2024:GECCO,
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author = "Shakiba Shahbandegan and Emily Dolson",
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title = "On the robustness of lexicase selection to
contradictory objectives",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Dimo Brockhoff and Tapabrata Ray 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",
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pages = "594--602",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, lexicase
selection, eco-evolutionary theory, many-objective
optimization, Evolutionary Multiobjective
Optimization",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654215",
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size = "9 pages",
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abstract = "Lexicase and epsilon-lexicase selection are state of
the art parent selection techniques for problems
featuring multiple selection criteria. Originally,
lexicase selection was developed for cases where these
selection criteria are unlikely to be in conflict with
each other, but preliminary work suggests it is also a
highly effective many-objective optimization algorithm.
However, to predict whether these results generalize,
we must understand lexicase selection's performance on
contradictory objectives. Prior work has shown mixed
results on this question. Here, we develop theory
identifying circumstances under which lexicase
selection will succeed or fail to find a Pareto-optimal
solution. To make this analysis tractable, we restrict
our investigation to a theoretical problem with
maximally contradictory objectives. Ultimately, we find
that lexicase and epsilon-lexicase selection each have
a region of parameter space where they are incapable of
optimizing contradictory objectives. Outside of this
region, however, they perform well despite the presence
of contradictory objectives. Based on these findings,
we propose theoretically-backed guidelines for
parameter choice. Additionally, we identify other
properties that may affect whether a many-objective
optimization problem is a good fit for lexicase or
epsilon-lexicase selection.",
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notes = "GECCO-2024 EMO A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Shakiba Shahbandegan
Emily Dolson
Citations