Exploring extended particle swarms: a genetic
programming approach
R. Poli, C. Di Chio, und W. Langdon. GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation, 1, Seite 169--176. Washington DC, USA, ACM Press, (25-29 June 2005)
Zusammenfassung
Particle Swarm Optimisation (PSO) uses a population of
particles fly over the fitness landscape in search of
an optimal solution. The particles are controlled by
forces that encourage each particle to fly back both
towards the best point sampled by it and towards the
swarm's best point, while its momentum tries to keep it
moving in its current direction.
Previous research poli:2005:eurogp started
exploring the possibility of evolving the force
generating equations which control the particles
through the use of genetic programming (GP).
We independently verify the findings of
poli:2005:eurogp and then extend it by
considering additional meaningful ingredients for the
PSO force-generating equations, such as global measures
of dispersion and position of the swarm. We show that,
on a range of problems, GP can automatically generate
new PSO algorithms that outperform standard
human-generated as well as some previously evolved
ones.
GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
Jahr
2005
Monat
25-29 June
Seiten
169--176
Verlag
ACM Press
Band
1
organisation
ACM SIGEVO (formerly ISGEC)
publisher_address
New York, NY, 10286-1405, USA
size
8 pages
isbn
1-59593-010-8
notes
GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052, XPS, ACM gecco-2005 key
1068036
%0 Conference Paper
%1 dichio:2005:gecco
%A Poli, Riccardo
%A Di Chio, Cecilia
%A Langdon, William B.
%B GECCO 2005: Proceedings of the 2005 conference on
Genetic and evolutionary computation
%C Washington DC, USA
%D 2005
%E Beyer, Hans-Georg
%E O'Reilly, Una-May
%E Arnold, Dirk V.
%E Banzhaf, Wolfgang
%E Blum, Christian
%E Bonabeau, Eric W.
%E Cantu-Paz, Erick
%E Dasgupta, Dipankar
%E Deb, Kalyanmoy
%E Foster, James A.
%E de
Jong, Edwin D.
%E Lipson, Hod
%E Llora, Xavier
%E Mancoridis, Spiros
%E Pelikan, Martin
%E Raidl, Guenther R.
%E Soule, Terence
%E Tyrrell, Andy M.
%E Watson, Jean-Paul
%E Zitzler, Eckart
%I ACM Press
%K Intelligence, PSO, Swarm algorithms, genetic optimisation, particle performance programming, swarm
%P 169--176
%T Exploring extended particle swarms: a genetic
programming approach
%U http://doi.acm.org/10.1145/1068009.1068036
%V 1
%X Particle Swarm Optimisation (PSO) uses a population of
particles fly over the fitness landscape in search of
an optimal solution. The particles are controlled by
forces that encourage each particle to fly back both
towards the best point sampled by it and towards the
swarm's best point, while its momentum tries to keep it
moving in its current direction.
Previous research poli:2005:eurogp started
exploring the possibility of evolving the force
generating equations which control the particles
through the use of genetic programming (GP).
We independently verify the findings of
poli:2005:eurogp and then extend it by
considering additional meaningful ingredients for the
PSO force-generating equations, such as global measures
of dispersion and position of the swarm. We show that,
on a range of problems, GP can automatically generate
new PSO algorithms that outperform standard
human-generated as well as some previously evolved
ones.
%@ 1-59593-010-8
@inproceedings{dichio:2005:gecco,
abstract = {Particle Swarm Optimisation (PSO) uses a population of
particles fly over the fitness landscape in search of
an optimal solution. The particles are controlled by
forces that encourage each particle to fly back both
towards the best point sampled by it and towards the
swarm's best point, while its momentum tries to keep it
moving in its current direction.
Previous research \cite{poli:2005:eurogp} started
exploring the possibility of evolving the force
generating equations which control the particles
through the use of genetic programming (GP).
We independently verify the findings of
\cite{poli:2005:eurogp} and then extend it by
considering additional meaningful ingredients for the
PSO force-generating equations, such as global measures
of dispersion and position of the swarm. We show that,
on a range of problems, GP can automatically generate
new PSO algorithms that outperform standard
human-generated as well as some previously evolved
ones.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Washington DC, USA},
author = {Poli, Riccardo and {Di Chio}, Cecilia and Langdon, William B.},
biburl = {https://www.bibsonomy.org/bibtex/2924afc7cab84a865c2dcb5237f7f5e84/brazovayeye},
booktitle = {{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation},
editor = {Beyer, Hans-Georg and O'Reilly, Una-May and Arnold, Dirk V. and Banzhaf, Wolfgang and Blum, Christian and Bonabeau, Eric W. and Cantu-Paz, Erick and Dasgupta, Dipankar and Deb, Kalyanmoy and Foster, James A. and {de
Jong}, Edwin D. and Lipson, Hod and Llora, Xavier and Mancoridis, Spiros and Pelikan, Martin and Raidl, Guenther R. and Soule, Terence and Tyrrell, Andy M. and Watson, Jean-Paul and Zitzler, Eckart},
interhash = {20c3a48e9729ac517116bc911f626366},
intrahash = {924afc7cab84a865c2dcb5237f7f5e84},
isbn = {1-59593-010-8},
keywords = {Intelligence, PSO, Swarm algorithms, genetic optimisation, particle performance programming, swarm},
month = {25-29 June},
notes = {GECCO-2005 A joint meeting of the fourteenth
international conference on genetic algorithms
(ICGA-2005) and the tenth annual genetic programming
conference (GP-2005).
ACM Order Number 910052, XPS, ACM gecco-2005 key
1068036},
organisation = {ACM SIGEVO (formerly ISGEC)},
pages = {169--176},
publisher = {ACM Press},
publisher_address = {New York, NY, 10286-1405, USA},
size = {8 pages},
timestamp = {2008-06-19T17:38:40.000+0200},
title = {Exploring extended particle swarms: a genetic
programming approach},
url = {http://doi.acm.org/10.1145/1068009.1068036},
volume = 1,
year = 2005
}