PSXO: Population-wide Semantic Crossover
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Vanneschi:2017:GECCO,
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author = "Leonardo Vanneschi and Mauro Castelli and
Luca Manzoni and Krzysztof Krawiec and Alberto Moraglio and
Sara Silva and Ivo Goncalves",
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title = "{PSXO}: Population-wide Semantic Crossover",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "257--258",
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size = "2 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3076003",
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DOI = "doi:10.1145/3067695.3076003",
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acmid = "3076003",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, inverse
matrix, population-wide crossover, real-life problems,
semantics",
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month = "15-19 " # jul,
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abstract = "Since its introduction, Geometric Semantic Genetic
Programming (GSGP) has been the inspiration to ideas on
how to reach optimal solutions efficiently. Among
these, in 2016 Pawlak has shown how to analytically
construct optimal programs by means of a linear
combination of a set of random programs. Given the
simplicity and excellent results of this method (LC)
when compared to GSGP, the author concluded that GSGP
is overkill. However, LC has limitations, and it was
tested only on simple benchmarks. In this paper, we
introduce a new method, Population-Wide Semantic
Crossover (PSXO), also based on linear combinations of
random programs, that overcomes these limitations. We
test the first variant (Inv) on a diverse set of
complex real-life problems, comparing it to LC, GSGP
and standard GP. We realize that, on the studied
problems, both LC and Inv are outperformed by GSGP, and
sometimes also by standard GP. This leads us to the
conclusion that GSGP is not overkill. We also introduce
a second variant (GPinv) that integrates evolution with
the approximation of optimal programs by means of
linear combinations. GPinv outperforms both LC and Inv
on unseen test data for the studied problems.",
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notes = "Also known as
\cite{Vanneschi:2017:PPS:3067695.3076003},
\cite{vanneschi2017psxo} GECCO-2017 A Recombination of
the 26th International Conference on Genetic Algorithms
(ICGA-2017) and the 22nd Annual Genetic Programming
Conference (GP-2017)",
- }
Genetic Programming entries for
Leonardo Vanneschi
Mauro Castelli
Luca Manzoni
Krzysztof Krawiec
Alberto Moraglio
Sara Silva
Ivo Goncalves
Citations