Fitness Landscape Analysis of Automated Machine Learning Search Spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Pimenta:2020:evoCOP,
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author = "Cristiano G. Pimenta and Alex G. C. {de Sa} and
Gabriela Ochoa and Gisele L. Pappa",
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title = "Fitness Landscape Analysis of Automated Machine
Learning Search Spaces",
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booktitle = "European Conference on Evolutionary Computation in
Combinatorial Optimization (EvoCOP 2020)",
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year = "2020",
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editor = "L. Paquete and C. Zarges",
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volume = "12102",
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series = "Lecture Notes in Computer Science",
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pages = "114--130",
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address = "Seville, Spain",
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month = "15-17 " # apr,
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organisation = "EvoStar, Species",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, TPOT, AutoML,
Fitness landscape analysis, Automated Machine Learning,
Fitness distance correlation, Neutrality",
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isbn13 = "978-3-030-43679-7",
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DOI = "doi:10.1007/978-3-030-43680-3_8",
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abstract = "The field of Automated Machine Learning (AutoML) has
as its main goal to automate the process of creating
complete Machine Learning (ML) pipelines to any dataset
without requiring deep user expertise in ML. Several
AutoML methods have been proposed so far, but there is
not a single one that really stands out. Furthermore,
there is a lack of studies on the characteristics of
the fitness landscape of AutoML search spaces. Such
analysis may help to understand the performance of
different optimization methods for AutoML and how to
improve them. This paper adapts classic fitness
landscape analysis measures to the context of AutoML.
This is a challenging task, as AutoML search spaces
include discrete, continuous, categorical and
conditional hyperparameters. We propose an ML pipeline
representation, a neighborhood definition and a
distance metric between pipelines, and use them in the
evaluation of the fitness distance correlation (FDC)
and the neutrality ratio for a given AutoML search
space. Results of FDC are counter-intuitive and require
a more in-depth analysis of a range of search spaces.
Results of neutrality, in turn, show a strong positive
correlation between the mean neutrality ratio and the
fitness value.",
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notes = "http://www.evostar.org/2020/ EvoCOP2020 held in
conjunction with EuroGP'2020, EvoMusArt2020 and
EvoApplications2020",
- }
Genetic Programming entries for
Cristiano Guimaraes Pimenta
Alex G C de Sa
Gabriela Ochoa
Gisele L Pappa
Citations