Alignment-based genetic programming for real life applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Vanneschi:2019:swarmEC,
-
author = "Leonardo Vanneschi and Mauro Castelli and
Kristen Scott and Leonardo Trujillo",
-
title = "Alignment-based genetic programming for real life
applications",
-
journal = "Swarm and Evolutionary Computation",
-
year = "2019",
-
volume = "44",
-
pages = "840--851",
-
month = feb,
-
keywords = "genetic algorithms, genetic programming, Geometric
semantic operators, Alignment, Error space, Real-life
applications",
-
ISSN = "2210-6502",
-
URL = "http://www.sciencedirect.com/science/article/pii/S2210650218300208",
-
DOI = "doi:10.1016/j.swevo.2018.09.006",
-
size = "12 pages",
-
abstract = "A recent discovery has attracted the attention of many
researchers in the field of genetic programming: given
individuals with particular characteristics of
alignment in the error space, called optimally aligned,
it is possible to reconstruct a globally optimal
solution. Furthermore, recent preliminary experiments
have shown that an indirect search consisting of
looking for optimally aligned individuals can have
benefits in terms of generalization ability compared to
a direct search for optimal solutions. For this reason,
defining genetic programming systems that look for
optimally aligned individuals is becoming an ambitious
and important objective. Nevertheless, the systems that
have been introduced so far present important
limitations that make them unusable in practice,
particularly for complex real-life applications. In
this paper, we overcome those limitations, and we
present the first usable alignment-based genetic
programming system, called nested alignment genetic
programming (NAGP). The presented...",
-
notes = "also known as \cite{VANNESCHI2018}",
- }
Genetic Programming entries for
Leonardo Vanneschi
Mauro Castelli
Kristen M Scott
Leonardo Trujillo
Citations