An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
Created by W.Langdon from
gp-bibliography.bib Revision:1.5619
- @InProceedings{Evans:2020:CEC,
-
author = "Benjamin Evans and Bing Xue and Mengjie Zhang",
-
booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
-
title = "An Adaptive and Near Parameter-free Evolutionary
Computation Approach Towards True Automation in
{AutoML}",
-
year = "2020",
-
editor = "Yaochu Jin",
-
month = jul,
-
keywords = "genetic algorithms, genetic programming, Sociology,
Statistics, Pipelines, Machine learning, Evolutionary
computation, Optimization, Automation",
-
isbn13 = "978-1-7281-6929-3",
-
DOI = "
doi:10.1109/CEC48606.2020.9185770",
-
abstract = "A common claim of evolutionary computation methods is
that they can achieve good results without the need for
human intervention. However, one criticism of this is
that there are still hyperparameters which must be
tuned in order to achieve good performance. In this
work, we propose a near parameter-free genetic
programming approach, which adapts the hyperparameter
values throughout evolution without ever needing to be
specified manually. We apply this to the area of
automated machine learning (by extending TPOT), to
produce pipelines which can effectively be claimed to
be free from human input, and show that the results are
competitive with existing state-of-the-art which use
hand-selected hyperparameter values. Pipelines begin
with a randomly chosen estimator and evolve to
competitive pipelines automatically. This work moves
towards a truly automated approach to AutoML.",
-
notes = "Also known as \cite{9185770}",
- }
Genetic Programming entries for
Benjamin Evans
Bing Xue
Mengjie Zhang
Citations