Evolutionary Neural Trees for Modeling and Predicting Complex Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{zhang:1997:entmpcs,
-
author = "Byoung-Tak Zhang and Peter Ohm and Heinz Muehlenbein",
-
title = "Evolutionary Neural Trees for Modeling and Predicting
Complex Systems",
-
journal = "Engineering Applications of Artificial Intelligence",
-
year = "1997",
-
volume = "10",
-
number = "5",
-
pages = "473--483",
-
month = oct,
-
keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, neurocomputing, evolutionary neural trees,
machine learning, system identification, complex
systems, time series prediction",
-
ISSN = "0952-1976",
-
DOI = "doi:10.1016/S0952-1976(96)00018-8",
-
size = "11 pages",
-
abstract = "Modelling and predicting the behaviour of many
technical systems is complicated because they are
generally characterised by a large number of variables,
parameters and interactions, and limited amounts of
collected data. This paper investigates an evolutionary
method for learning models of such systems. The models
thus evolved are based on trees of heterogeneous neural
units. The set of different neuron types is defined by
the application domain, and the specific type of each
unit is determined during the evolutionary learning
process. The structure, size, and weights of the neural
trees are also adapted by evolution. Since the genetic
search used for training does not require error
derivatives, a wide range of neural models can be
constructed. This generality is contrasted with various
existing methods for complex system modeling, which
investigate only restricted topological subsets rather
than the complete class of architectures. An
improvement in the predictive accuracy and parsimony of
models is reported, against backpropagation networks
and other well-engineered polynomial-based methods for
two problems: MacKey-Glass and Lorenz-like chaotic
systems. The authors also demonstrate the importance of
the selection pressure towards model parsimony for the
improvement of prediction accuracy.",
- }
Genetic Programming entries for
Byoung-Tak Zhang
Peter Ohm
Heinz Muhlenbein
Citations