Evolving Signal Processing Algorithms by Genetic
Programming
K. Sharman, A. Esparcia Alcazar, and Y. Li. First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA, 414, page 473--480. Sheffield, UK, IEE, (12-14 September 1995)
Abstract
We introduce a novel Genetic Programming (GP)
technique to evolve both the structure and parameters
of adaptive Digital Signal Processing (DSP) algorithms.
This is accomplished by defining a set of node
functions and terminals to implement the basic
operations commonly used in a large class of DSP
algorithms. In addition, we show how Simulated
Annealing may be employed to assist the GP in
optimising the numerical parameters of expression
trees. The concepts are illustrated by using GP to
evolve high performance algorithms for detecting binary
data sequences at the output of a noisy, non-linear
communications channel.
First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA
year
1995
month
12-14 September
pages
473--480
publisher
IEE
volume
414
publisher_address
London, UK
isbn
0-85296-650-4
notes
12--14 September 1995, Halifax Hall, University of
Sheffield, UK see also
http://www.iee.org.uk/LSboard/Conf/program/galprog.htm
Use simulated annealing to assist GP optimise numerical
componets of expression trees.
DSP uses push and pop (any point in stack stkN) in
function set
Previous outputs (Yn) used as inputs - "time
recursion". Single sample delay node Z. "infinitley
many different tree structures for a particular system
function"
"Time varying fitness function" Sigmoidal transfer
function with beta evolable.
Pop 250, tournament 10, max gene 100, mutation, 2 ADFs,
Evolved filter has zero bit error rate!
"Perliminary results...GP significantly out performed
existing systems."
GP computationaly expensive
%0 Conference Paper
%1 sharman:1995:espa
%A Sharman, Ken C.
%A Esparcia Alcazar, Anna I.
%A Li, Yun
%B First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA
%C Sheffield, UK
%D 1995
%E Zalzala, A. M. S.
%I IEE
%K adaptive algorithms, annealing, genetic memory networks, neural processing, programming, signal simulated
%P 473--480
%T Evolving Signal Processing Algorithms by Genetic
Programming
%U http://www.iti.upv.es/~anna/papers/galesi95.ps
%V 414
%X We introduce a novel Genetic Programming (GP)
technique to evolve both the structure and parameters
of adaptive Digital Signal Processing (DSP) algorithms.
This is accomplished by defining a set of node
functions and terminals to implement the basic
operations commonly used in a large class of DSP
algorithms. In addition, we show how Simulated
Annealing may be employed to assist the GP in
optimising the numerical parameters of expression
trees. The concepts are illustrated by using GP to
evolve high performance algorithms for detecting binary
data sequences at the output of a noisy, non-linear
communications channel.
%@ 0-85296-650-4
@inproceedings{sharman:1995:espa,
abstract = {We introduce a novel Genetic Programming (GP)
technique to evolve both the structure and parameters
of adaptive Digital Signal Processing (DSP) algorithms.
This is accomplished by defining a set of node
functions and terminals to implement the basic
operations commonly used in a large class of DSP
algorithms. In addition, we show how Simulated
Annealing may be employed to assist the GP in
optimising the numerical parameters of expression
trees. The concepts are illustrated by using GP to
evolve high performance algorithms for detecting binary
data sequences at the output of a noisy, non-linear
communications channel.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Sheffield, UK},
author = {Sharman, Ken C. and {Esparcia Alcazar}, Anna I. and Li, Yun},
biburl = {https://www.bibsonomy.org/bibtex/2b177ce990686d3565bed06d23c4953a8/brazovayeye},
booktitle = {First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA},
editor = {Zalzala, A. M. S.},
interhash = {c909a247619eef45f3c7be19f6e757d8},
intrahash = {b177ce990686d3565bed06d23c4953a8},
isbn = {0-85296-650-4},
keywords = {adaptive algorithms, annealing, genetic memory networks, neural processing, programming, signal simulated},
month = {12-14 September},
notes = {12--14 September 1995, Halifax Hall, University of
Sheffield, UK see also
http://www.iee.org.uk/LSboard/Conf/program/galprog.htm
Use simulated annealing to assist GP optimise numerical
componets of expression trees.
DSP uses push and pop (any point in stack stkN) in
function set
Previous outputs (Yn) used as inputs - {"}time
recursion{"}. Single sample delay node Z. {"}infinitley
many different tree structures for a particular system
function{"}
{"}Time varying fitness function{"} Sigmoidal transfer
function with beta evolable.
Pop 250, tournament 10, max gene 100, mutation, 2 ADFs,
Evolved filter has zero bit error rate!
{"}Perliminary results...GP significantly out performed
existing systems.{"}
GP computationaly expensive},
pages = {473--480},
publisher = {IEE},
publisher_address = {London, UK},
timestamp = {2008-06-19T17:51:34.000+0200},
title = {Evolving Signal Processing Algorithms by Genetic
Programming},
url = {http://www.iti.upv.es/~anna/papers/galesi95.ps},
volume = 414,
year = 1995
}