6 research outputs found
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
The deployment of Machine Learning (ML) models is a difficult and
time-consuming job that comprises a series of sequential and correlated tasks
that go from the data pre-processing, and the design and extraction of
features, to the choice of the ML algorithm and its parameterisation. The task
is even more challenging considering that the design of features is in many
cases problem specific, and thus requires domain-expertise. To overcome these
limitations Automated Machine Learning (AutoML) methods seek to automate, with
few or no human-intervention, the design of pipelines, i.e., automate the
selection of the sequence of methods that have to be applied to the raw data.
These methods have the potential to enable non-expert users to use ML, and
provide expert users with solutions that they would unlikely consider. In
particular, this paper describes AutoML-DSGE - a novel grammar-based framework
that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of
Scikit-Learn classification pipelines. The experimental results include
comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient
ClassificationPipeline Evolution (RECIPE), and show that the average
performance of the classification pipelines generated by AutoML-DSGE is always
superior to the average performance of RECIPE; the differences are
statistically significant in 3 out of the 10 used datasets.Comment: EvoApps 202
GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution
4noGene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a recent Evolutionary Algorithm (EA) in which the interactions among parts of the solution (i.e., the linkage) are learned and exploited in a novel variation operator. We present GOMGE, the extension of GOMEA to Grammatical Evolution (GE), a popular EA based on an indirect representation which may be applied to any problem whose solutions can be described using a context-free grammar (CFG). GE is a general approach that does not require the user to tune the internals of the EA to fit the problem at hand: there is hence the opportunity for benefiting from the potential of GOMEA to automatically learn and exploit the linkage. We apply the proposed approach to three variants of GE differing in the representation (original GE, SGE, and WHGE) and incorporate in GOMGE two specific improvements aimed at coping with the high degeneracy of those representations. We experimentally assess GOMGE and show that, when coupled with WHGE and SGE, it is clearly beneficial to both effectiveness and efficiency, whereas it delivers mixed results with the original GE.partially_openembargoed_20190822Medvet, Eric; Bartoli, Alberto; De Lorenzo, Andrea; Tarlao, FabianoMedvet, Eric; Bartoli, Alberto; De Lorenzo, Andrea; Tarlao, Fabian