A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{gruau:1996:ceVdeGNN,
-
author = "Frederic Gruau and Darrell Whitley and Larry Pyeatt",
-
title = "A Comparison between Cellular Encoding and Direct
Encoding for Genetic Neural Networks",
-
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
-
editor = "John R. Koza and David E. Goldberg and
David B. Fogel and Rick L. Riolo",
-
year = "1996",
-
month = "28--31 " # jul,
-
keywords = "genetic algorithms, genetic programming",
-
pages = "81--89",
-
address = "Stanford University, CA, USA",
-
publisher = "MIT Press",
-
ISBN = "9780-262-31587-6",
-
URL = "http://www.cs.colostate.edu/~genitor/1996/gp96.ps.gz",
-
URL = "https://dl.acm.org/doi/10.5555/1595536.1595547",
-
URL = "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap10.pdf",
-
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
-
DOI = "doi:10.7551/mitpress/3242.003.0013",
-
size = "9 pages",
-
abstract = "This paper compares the efficiency of two encoding
schemes for Artificial Neural Networks optimized by
evolutionary algorithms. Direct Encoding encodes the
weights for an a priori fixed neural network
architecture. Cellular Encoding encodes both weights
and the architecture of the neural network. In previous
studies, Direct Encoding and Cellular Encoding have
been used to create neural networks for balancing 1 and
2 poles attached to a cart on a fixed track. The poles
are balanced by a controller that pushes the cart to
the left or the right. In some cases velocity
information about the pole and cart is provided as an
input; in other cases the network must learn to balance
a single pole without velocity information. A careful
study of the behavior of these systems suggests that it
is possible to balance a single pole with velocity
information as an input and without learning to compute
the velocity. A new fitness function is introduced that
forces the neural network to compute the velocity. By
using this new fitness function and tuning the
syntactic constraints used with cellular encoding, we
achieve a tenfold speedup over our previous study and
solve a more difficult problem: balancing two poles
when no information about the velocity is provided as
input.",
-
notes = "See also \cite{HeidrichMeisner2009152}. GP-96",
- }
Genetic Programming entries for
Frederic Gruau
L Darrell Whitley
Larry D Pyeatt
Citations