MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Zhang:2023:EuroGP,
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author = "Hengzhe Zhang and Qi Chen and Alberto Tonda and
Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
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title = "{MAP}-Elites with Cosine-Similarity for Evolutionary
Ensemble Learning",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "84--100",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Evolutionary
ensemble learning, Quality diversity optimization,
Multi-dimensional Archive of Phenotypic Elites",
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isbn13 = "978-3-031-29572-0",
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URL = "https://rdcu.be/c8UP0",
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DOI = "doi:10.1007/978-3-031-29573-7_6",
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size = "17 pages",
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abstract = "Evolutionary ensemble learning methods with Genetic
Programming have achieved remarkable results on
regression and classification tasks by employing
quality-diversity optimization techniques like
MAP-Elites and Neuro-MAP-Elites. The MAP-Elites
algorithm uses dimensionality reduction methods, such
as variational auto-encoders, to reduce the
high-dimensional semantic space of genetic programming
to a two-dimensional behavioral space. Then, it
constructs a grid of high-quality and diverse models to
form an ensemble model. In MAP-Elites, however,
variational auto-encoders rely on Euclidean space
topology, which is not effective at preserving
high-quality individuals. To solve this problem, this
paper proposes a principal component analysis method
based on a cosine-kernel for dimensionality reduction.
In order to deal with unbalanced distributions of good
individuals, we propose a zero-cost reference points
synthesizing method. Experimental results on 108
datasets show that combining principal component
analysis using a cosine kernel with reference points
significantly improves the performance of the
MAP-Elites evolutionary ensemble learning algorithm.",
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notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in
conjunction with EvoCOP2023, EvoMusArt2023 and
EvoApplications2023",
- }
Genetic Programming entries for
Hengzhe Zhang
Qi Chen
Alberto Tonda
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations