Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Mambrini:2013:CEC,
-
article_id = "1697",
-
author = "Andrea Mambrini and Luca Manzoni and
Alberto Moraglio",
-
title = "Theory-Laden Design of Mutation-Based Geometric
Semantic Genetic Programming for Learning
Classification Trees",
-
booktitle = "2013 IEEE Conference on Evolutionary Computation",
-
volume = "1",
-
year = "2013",
-
month = jun # " 20-23",
-
editor = "Luis Gerardo {de la Fraga}",
-
pages = "416--423",
-
address = "Cancun, Mexico",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-1-4799-0453-2",
-
DOI = "doi:10.1109/CEC.2013.6557599",
-
size = "8 pages",
-
abstract = "Geometric Semantic Genetic Programming (GSGP) is a
recently introduced framework to design domain-specific
search operators for Genetic Programming (GP) to search
directly the semantic space of functions. The fitness
landscape seen by GSGP is always - for any domain and
for any problem - unimodal with a constant slope by
construction. This makes the search for the optimum
much easier than for traditional GP, and it opens the
way to analyse theoretically in a easy manner the
optimisation time of GSGP in a general setting. We
design and analyse a mutation-based GSGP for the class
of all classification tree learning problems, which is
a classic GP application domain.",
-
notes = "CEC 2013 - A joint meeting of the IEEE, the EPS and
the IET.",
- }
Genetic Programming entries for
Andrea Mambrini
Luca Manzoni
Alberto Moraglio
Citations