Designing Black-Box Optimizers with PushGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{stanovov:2024:GECCOcomp,
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author = "Vladimir Stanovov",
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title = "Designing {Black-Box} Optimizers with {PushGP}",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
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year = "2024",
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editor = "Ting Hu and Aniko Ekart",
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pages = "535--538",
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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,
hyper-heuristic, numerical optimization: Poster",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3654172",
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size = "4 pages",
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abstract = "This study is focused on an attempt to design
general-purpose black-box population-based numerical
optimization algorithms from scratch using the Push
language and a genetic programming approach. The
designed Push programs are capable of performing
operations on vectors, thus allowing creating
algorithms suitable for problems of any dimension. The
programs are applied to each of the individuals of the
population, and the interaction between individuals is
possible. The training is performed on the
single-objective benchmark on numerical optimization
presented within the Congress on Evolutionary
Computation 2017. It is shown that with significant
computational effort such approach is capable of
creating unique optimization methods, which could be
competitive to some of the known optimization tools.",
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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
Vladimir Stanovov
Citations