Evolutionary Algorithms for Segment Optimization in Vectorial GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{fleck:2023:GECCOcomp,
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author = "Philipp Fleck and Stephan Winkler and
Michael Kommenda and Sara Silva and Leonardo Vanneschi and
Michael Affenzeller",
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title = "Evolutionary Algorithms for Segment Optimization in
Vectorial {GP}",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "439--442",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # 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, evolutionary
algorithms, vectorial, symbolic regression: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590668",
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size = "4 pages",
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abstract = "Vectorial Genetic Programming (Vec-GP) extends regular
GP by allowing vectorial input features (e.g. time
series data), while retaining the expressiveness and
interpretability of regular GP. The availability of raw
vectorial data during training, not only enables Vec-GP
to select appropriate aggregation functions itself, but
also allows Vec-GP to extract segments from vectors
prior to aggregation (like windows for time series
data). This is a critical factor in many machine
learning applications, as vectors can be very long and
only small segments may be relevant. However, allowing
aggregation over segments within GP models makes the
training more complicated. We explore the use of common
evolutionary algorithms to help GP identify appropriate
segments, which we analyze using a simplified problem
that focuses on optimizing aggregation segments on
fixed data. Since the studied algorithms are to be used
in GP for local optimization (e.g. as mutation
operator), we evaluate not only the quality of the
solutions, but also take into account the convergence
speed and anytime performance. Among the evaluated
algorithms, CMA-ES, PSO and ALPS show the most
promising results, which would be prime candidates for
evaluation within GP.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Philipp Fleck
Stephan M Winkler
Michael Kommenda
Sara Silva
Leonardo Vanneschi
Michael Affenzeller
Citations