Evolving Workflow Graphs Using Typed Genetic Programming

0455775 - ÚI 2016 RIV US eng C - Conference Paper (international conference)
Křen, T. - Pilát, M. - Neruda, Roman
Evolving Workflow Graphs Using Typed Genetic Programming.
SSCI 2015 IEEE Symposium Series on Computational Intelligence. Los Alamitos: IEEE, 2015, s. 1407-1414. ISBN 978-1-4799-7560-0.
[SSCI 2015. Symposium Series on Computational Intelligence. Cape Town (ZA), 08.12.2015-10.12.2015]
R&D Projects: GA ČR GA15-19877S; GA MŠMT ED1.1.00/02.0070
Grant - others:GA UK(CZ) 187115; GA UK(CZ) SVV 260224; GA MŠk(CZ) LM2011033
Institutional support: RVO:67985807
Keywords : typed genetic programming * meta-learning * workflow graphs
Subject RIV: IN - Informatics, Computer Science

When applying machine learning techniques to more complicated datasets, it is often beneficial to use ensembles of simpler models instead of a single, more complicated, model. However, the creation of ensembles is a tedious task which requires a lot of human interaction and experimentation. In this paper, we present a technique for construction of ensembles based on typed genetic programming. The technique describes an ensemble as a directed acyclic graph, which is internally represented as a tree evolved by the genetic programming. The approach is evaluated in a series of experiments on various datasets and compared to the performance of simple models tuned by grid search, as well as to ensembles generated in a systematic manner.
Permanent Link: http://hdl.handle.net/11104/0256398