- author = "Michael Kommenda and Michael Affenzeller and Bogdan Burlacu and Gabriel Kronberger and Stephan M. Winkler",
- title = "Genetic programming with data migration for symbolic regression",
- booktitle = "GECCO 2014 Workshop on Symbolic Regression and Modelling",
- year = "2014",
- editor = "Steven Gustafson and Ekaterina Vladislavleva",
- isbn13 = "978-1-4503-2881-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "1361--1366",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Vancouver, BC, Canada",
-
URL = "
http://doi.acm.org/10.1145/2598394.2609857",
-
DOI = "
doi:10.1145/2598394.2609857",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "In this publication genetic programming (GP) with data
migration for symbolic regression is presented. The
motivation for the development of the algorithm is to
evolve models which generalise well on previously
unseen data. GP with data migration uses multiple
subpopulations to maintain the genetic diversity during
the algorithm run and a sophisticated training subset
selection strategy. Each subpopulation is evaluated on
a different fixed training subset (FTS) and
additionally a variable training subset (VTS) is
exchanged between the subpopulations at specific data
migration intervals. Thus, the individuals are
evaluated on the unification of FTS and VTS and should
have better generalisation properties due to the
regular changes of the VTS.
The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.",
- notes = "Also known as \cite{2609857} Distributed at GECCO-2014.",
