Evolving multidimensional transformations for symbolic regression with M3GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Munoz:2019:MemeticC,
-
author = "Luis Munoz and Leonardo Trujillo and Sara Silva and
Mauro Castelli and Leonardo Vanneschi",
-
title = "Evolving multidimensional transformations for symbolic
regression with {M3GP}",
-
journal = "Memetic Computing",
-
year = "2019",
-
volume = "11",
-
number = "2",
-
pages = "111--126",
-
month = jun,
-
keywords = "genetic algorithms, genetic programming, Symbolic
regression, Data transformation, Feature optimization",
-
ISSN = "1865-9284",
-
DOI = "doi:10.1007/s12293-018-0274-5",
-
size = "16 pages",
-
abstract = "Multidimensional Multiclass Genetic Programming with
Multidimensional Populations (M3GP) was originally
proposed as a wrapper approach for supervised
classification. M3GP searches for transformations of
the form k:R^p->R^d, where p is the number of
dimensions of the problem data, and d is the
dimensionality of the transformed data, as determined
by the search. This work extends M3GP to symbolic
regression, building models that are linear in the
parameters using the transformed data. The proposal
implements a sequential memetic structure with
Lamarckian inheritance, combining two local search
methods: a greedy pruning algorithm and least squares
parameter estimation. Experimental results show that
M3GP outperforms several standard and state-of-the-art
regression techniques, as well as other GP approaches.
Using several synthetic and real-world problems, M3GP
outperforms most methods in terms of RMSE and generates
more parsimonious models. The performance of M3GP can
be explained by the fact that M3GP increases the
maximal mutual information in the new feature space.",
- }
Genetic Programming entries for
Luis Munoz Delgado
Leonardo Trujillo
Sara Silva
Mauro Castelli
Leonardo Vanneschi
Citations